03_Meta-Optimization_Using_Many_Problems.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-Optimization Using Many Problems\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/MetaOps) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsl1877BS8m3yt8t_wq2IWji)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In the previous two papers, we tuned the parameters of Differential Evolution (DE) to work well on a single problem. This resulted in superior performance on that particular problem, but it may also cause worse performance on other problems.\n",
    "\n",
    "When developing a new optimization method, we need to provide the end-user with a good default choice of control parameters, that have been tuned to work well on a wide range of problems. This hopefully increases the chance that the optimizer will perform well on new problems in the future.\n",
    "\n",
    "We could use the Bayesian Meta-Optimizer from the [first paper](https://github.com/Hvass-Labs/MetaOps/blob/master/01_Bayesian_Meta-Optimization.ipynb) with multiple problems, simply by summing the meta-fitness for different problems into a single aggregate meta-fitness that must then be meta-optimized. The advantage of this approach is that it is very simple. The disadvantage is that different problems may have radically different fitness-ranges, which could cause harmful interference when summing their fitness values.\n",
    "\n",
    "Another approach which we will use in this paper, is to use Multi-Objective Meta-Optimization similar to the [second paper](https://github.com/Hvass-Labs/MetaOps/blob/master/02_Multi-Objective_Meta-Optimization.ipynb), but instead of having multiple objectives for a single problem, we will use several different benchmark problems. The implementation is very similar to the second paper and most of the source-code has simply been reused here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flowchart\n",
    "\n",
    "The flowchart below shows the rough idea. We use the NSGA-2 algorithm (Non-Dominated Sorting Genetic Algorithm version 2) to propose new parameters for the DE optimizer. We then perform e.g. 32 optimization runs on each of 5 problems using these parameters and record the mean fitness after some number of evaluations. These are the 5 meta-fitness measures that we send back to the NSGA-2 so it can evaluate how well the DE parameters performed and suggest new parameters to be tried. This is repeated until the best DE parameters have been found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Flowchart](images/03_flowchart_many_problems.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normal packages.\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import multiprocessing as mp\n",
    "import functools\n",
    "\n",
    "# Heuristic optimizers such as DE, PSO and NSGA-2.\n",
    "# This is actually implemented in C++ so it should run very fast.\n",
    "import pygmo as pg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.9'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Settings\n",
    "\n",
    "We will use benchmark problems from the CEC 2014 competition set. These are the available problems:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CEC2014 - f1(ellips_func)\n",
      "CEC2014 - f2(bent_cigar_func)\n",
      "CEC2014 - f3(discus_func)\n",
      "CEC2014 - f4(rosenbrock_func)\n",
      "CEC2014 - f5(ackley_func)\n",
      "CEC2014 - f6(weierstrass_func)\n",
      "CEC2014 - f7(griewank_func)\n",
      "CEC2014 - f8(rastrigin_func_non_rotated)\n",
      "CEC2014 - f9(rastrigin_func)\n",
      "CEC2014 - f10(schwefel_func_non_rotated)\n",
      "CEC2014 - f11(schwefel_func)\n",
      "CEC2014 - f12(katsuura_func)\n",
      "CEC2014 - f13(happycat_func)\n",
      "CEC2014 - f14(hgbat_func)\n",
      "CEC2014 - f15(grie_rosen_func)\n",
      "CEC2014 - f16(escaffer6_func)\n",
      "CEC2014 - f17(hf01)\n",
      "CEC2014 - f18(hf02)\n",
      "CEC2014 - f19(hf03)\n",
      "CEC2014 - f20(hf04)\n",
      "CEC2014 - f21(hf05)\n",
      "CEC2014 - f22(hf06)\n",
      "CEC2014 - f23(cf01)\n",
      "CEC2014 - f24(cf02)\n",
      "CEC2014 - f25(cf03)\n",
      "CEC2014 - f26(cf04)\n",
      "CEC2014 - f27(cf05)\n",
      "CEC2014 - f28(cf06)\n"
     ]
    }
   ],
   "source": [
    "for prob_id in range(1, 29):\n",
    "    problem = pg.problem(pg.problems.cec2014(prob_id=prob_id, dim=10))\n",
    "    print(problem.get_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dimensionality of each problem.\n",
    "dim = 10\n",
    "\n",
    "# List of problem id's we will be using.\n",
    "# These must be between 1 and 28 (inclusive).\n",
    "problem_ids = [1, 2, 3, 4, 5]\n",
    "\n",
    "# Number of problems.\n",
    "num_problems = len(problem_ids)\n",
    "\n",
    "# List of problem instances that must be optimized.\n",
    "problems = [pg.problem(pg.problems.cec2014(prob_id=id, dim=dim))\n",
    "            for id in problem_ids]\n",
    "\n",
    "# Number of fitness evaluations for one optimization run.\n",
    "num_fitness_evals = 10000\n",
    "\n",
    "# Number of optimization runs to perform on each problem, for each\n",
    "# new choice of parameters for the optimizer. This should not be\n",
    "# too low because the optimizer is stochastic so we need to average\n",
    "# out the performance over many runs. But it should not be too high.\n",
    "# This should be a multiple of the number of CPU cores because\n",
    "# we run it in parallel using all available CPU cores.\n",
    "num_runs = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer Parameters\n",
    "\n",
    "In this example, we will use [Differential Evolution (DE)](https://en.wikipedia.org/wiki/Differential_evolution) as the heuristic optimizer whose parameters we want to tune. We first need to setup the search-space dimensions for the parameters. We will use the NSGA-2 implementation from Pygmo as the meta-optimizer, which requires the search-space to be defined as floating-point arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names for the parameters.\n",
    "# This is actually not used in the code below, but is merely\n",
    "# included here as a reminder of the dimensions.\n",
    "dimension_names = [\"pop_size\", \"F\", \"CR\", \"variant\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of a default choice of parameters.\n",
    "default_parameters = [100, 0.8, 0.9, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lower and upper boundaries for the parameters.\n",
    "params_min = [5.0, 0.0, 0.0, 1.0]\n",
    "params_max = [200.0, 1.0, 1.0, 10.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_parameters(params):\n",
    "    \"\"\"\n",
    "    Unpack an array of floats to the individual parameters\n",
    "    of the proper type. Note that the parameters CR and F\n",
    "    are rounded to 2 decimals, which works together with\n",
    "    the meta_fitness() function and its decorator @lru_cache\n",
    "    as explained further below.\n",
    "    \"\"\"\n",
    "\n",
    "    pop_size = int(params[0] + 0.5)     # int\n",
    "    CR = round(params[1], 2)            # float with 2 decimals\n",
    "    F = round(params[2], 2)             # float with 2 decimals\n",
    "    variant = int(params[3] + 0.5)      # int\n",
    "    \n",
    "    return pop_size, CR, F, variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names of the optimizer variants used for printing.\n",
    "variant_names = \\\n",
    "{\n",
    "     1: \"best/1/exp\",\n",
    "     2: \"rand/1/exp\",\n",
    "     3: \"rand-to-best/1/exp\",\n",
    "     4: \"best/2/exp\",\n",
    "     5: \"rand/2/exp\",\n",
    "     6: \"best/1/bin\",\n",
    "     7: \"rand/1/bin\",\n",
    "     8: \"rand-to-best/1/bin\",\n",
    "     9: \"best/2/bin\",\n",
    "     10: \"rand/2/bin\"    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_parameters(pop_size, CR, F, variant):\n",
    "    \"\"\"Print the given parameters for the DE optimizer.\"\"\"\n",
    "\n",
    "    print(\"pop_size:\", pop_size)\n",
    "    print(\"CR: {0:.2f}\".format(CR))\n",
    "    print(\"F: {0:.2f}\".format(F))\n",
    "    print(\"variant: {0} ({1})\".format(variant,\n",
    "                                      variant_names[variant]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_parameters_raw(params):\n",
    "    \"\"\"\n",
    "    Print the given parameters for the DE optimizer.\n",
    "    The parameters are passed as a list of floats.\n",
    "    \"\"\"\n",
    "\n",
    "    # Unpack the parameters.\n",
    "    pop_size, CR, F, variant = unpack_parameters(params)\n",
    "\n",
    "    # Print them.\n",
    "    print_parameters(pop_size=pop_size, CR=CR, F=F, variant=variant)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel Optimization Runs\n",
    "\n",
    "Calculating the meta-fitness for a single problem, requires us to perform a number of optimization runs on that problem and average the best-found fitness values from those runs. To speed up the execution we perform these optimization runs in parallel.\n",
    "\n",
    "This could have been implemented more elegantly using a function `parallel_run()` with a sub-function `_single_run()` but that did not seem to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ParallelRunner:\n",
    "    \"\"\"Perform a number of optimization runs in parallel.\"\"\"\n",
    "    \n",
    "    def __init__(self, problem, num_generations,\n",
    "                 pop_size, CR, F, variant):\n",
    "        # Copy parameters for optimizer.\n",
    "        self.problem = problem\n",
    "        self.num_generations = num_generations\n",
    "        self.pop_size = pop_size\n",
    "        self.CR = CR\n",
    "        self.F = F\n",
    "        self.variant = variant\n",
    "\n",
    "    def _single_run(self, i):\n",
    "        \"\"\"\n",
    "        Perform a single optimization run and return the best fitness.\n",
    "        \n",
    "        :param i: Parallel job number. Ignored here.\n",
    "        :return: Best fitness found.\n",
    "        \"\"\"\n",
    "\n",
    "        # Create a new instance of the optimizer using\n",
    "        # the given parameters.\n",
    "        uda = pg.de(gen=self.num_generations,\n",
    "                    CR=self.CR, F=self.F, variant=self.variant)\n",
    "        optimizer = pg.algorithm(uda=uda)\n",
    "\n",
    "        # Create a population of agents for the optimizer.\n",
    "        population = pg.population(prob=self.problem,\n",
    "                                   size=self.pop_size)\n",
    "\n",
    "        # Evolve the optimizer's population.\n",
    "        population = optimizer.evolve(population)\n",
    "\n",
    "        # Best fitness found in this optimization run.\n",
    "        fitness = population.champion_f[0]\n",
    "\n",
    "        return fitness\n",
    "\n",
    "    def run(self):\n",
    "        \"\"\"Perform the optimization runs in parallel.\"\"\"\n",
    "\n",
    "        # Create a pool of workers sized according to the CPU cores available.\n",
    "        pool = mp.Pool()\n",
    "\n",
    "        # Calculate the fitness for each agent in parallel.\n",
    "        fitnesses = pool.map(self._single_run, range(num_runs))\n",
    "\n",
    "        # Close the pool of workers and wait for them all to finish.\n",
    "        pool.close()\n",
    "        pool.join()\n",
    "        \n",
    "        return fitnesses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-Fitness\n",
    "\n",
    "The meta-fitness function performs a number of optimization runs with the given choice of parameters and evaluates their performance on each of the benchmark problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "@functools.lru_cache()\n",
    "def meta_fitness(pop_size, CR, F, variant):\n",
    "    \"\"\"\n",
    "    Meta-fitness for evaluating the performance of the DE optimizer\n",
    "    with the given parameters on several benchmark problems.\n",
    "\n",
    "    Note the function-decorator @functools.lru_cache which\n",
    "    caches the results of previous calls to this function.\n",
    "    This is because the NSGA-2 meta-optimizer calls this\n",
    "    function with identical parameters many times, and although\n",
    "    the result is stochastic so it may improve slightly, it\n",
    "    wastes a lot of computation to calculate very similar\n",
    "    meta-fitness values for identical parameters. By using cached\n",
    "    results we save about 80% of the computation time.\n",
    "    To effectively use the caching, the parameters pop_size and\n",
    "    variant are integers and the parameters CR and F have been\n",
    "    rounded to 2 decimals in the unpack_parameters() function.\n",
    "\n",
    "    :param pop_size: Population size.\n",
    "    :param CR: Control-parameter CR (crossover probability).\n",
    "    :param F: Control-parameter F (differential weight).\n",
    "    :param variant: DE variant, integer between 1 and 10.\n",
    "    :return: Array of floats for the meta-fitness objectives.\n",
    "    \"\"\"\n",
    "\n",
    "    def _run(problem):\n",
    "        \"\"\"Helper-function for performing optimization runs.\"\"\"\n",
    "\n",
    "        # Create the job-runner for doing the optimization runs.\n",
    "        runner = ParallelRunner(problem=problem,\n",
    "                                num_generations=num_generations,\n",
    "                                pop_size=pop_size,\n",
    "                                CR=CR, F=F, variant=variant)\n",
    "        \n",
    "        # Perform the optimization runs in parallel.\n",
    "        # This returns a list of the best fitness-values found\n",
    "        # for each of the optimization runs.\n",
    "        fitnesses = runner.run()\n",
    "        \n",
    "        # The meta-fitness measure of how well the optimizer\n",
    "        # performed with those parameters, averaged over\n",
    "        # several optimization runs.\n",
    "        meta_fitness = np.mean(fitnesses)\n",
    "        \n",
    "        return meta_fitness\n",
    "\n",
    "    # Print parameters.\n",
    "    print_parameters(pop_size=pop_size, CR=CR, F=F, variant=variant)\n",
    "    \n",
    "    # Number of iterations for the optimizer's population.\n",
    "    # This ensures that we perform the same number of\n",
    "    # evaluations of the problem's fitness function\n",
    "    # regardless of the population-size.\n",
    "    num_generations = num_fitness_evals // pop_size\n",
    "\n",
    "    # Perform the optimization runs and get their meta-fitness.\n",
    "    meta_fitness = [_run(problem) for problem in problems]\n",
    "    meta_fitness = np.array(meta_fitness)\n",
    "\n",
    "    # Print the meta-fitness.\n",
    "    print(np.array2string(meta_fitness, precision=2))\n",
    "    print()\n",
    "\n",
    "    # Return array with the multi-objective meta-fitness value.\n",
    "    return meta_fitness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-Optimizer\n",
    "\n",
    "We use NSGA-2 as the meta-optimizer for tuning the parameters of the DE algorithm so as to perform well on the multiple meta-fitness measures we defined above.\n",
    "\n",
    "In order to get this working in Pygmo, we first need to wrap the above `meta_fitness()` function in this `MetaProblem` class that is compatible with Pygmo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetaProblem:\n",
    "    \"\"\"\n",
    "    Wrapper for the meta_fitness() function that connects it with\n",
    "    the meta-optimizer. This is necessary when using the pygmo API.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def fitness(self, params):\n",
    "        \"\"\"\n",
    "        Calculate and return the meta-fitness values for\n",
    "        the given parameters.\n",
    "        \"\"\"\n",
    "\n",
    "        # Unpack the parameters.\n",
    "        pop_size, CR, F, variant = unpack_parameters(params)\n",
    "        \n",
    "        # Calculate and return the meta-fitness values.\n",
    "        return meta_fitness(pop_size=pop_size,\n",
    "                            CR=CR, F=F, variant=variant)\n",
    "\n",
    "    def get_bounds(self):\n",
    "        \"\"\"Get boundaries of the parameter search-space.\"\"\"\n",
    "        return params_min, params_max\n",
    "\n",
    "    def get_nobj(self):\n",
    "        \"\"\"Get number of meta-fitness-objectives.\"\"\"\n",
    "        return num_problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the function for doing the actual meta-optimization. It creates an instance of the `MetaProblem` class we just defined, and then runs NSGA-2 on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meta_optimize(pop_size=40, num_generations=100):\n",
    "    \"\"\"\n",
    "    Perform meta-optimization using NSGA-2 to find the\n",
    "    best parameters of DE with regard to the\n",
    "    multi-objective meta-fitness measure defined above.\n",
    "    \n",
    "    :param pop_size: Population size for NSGA-2.\n",
    "    :param num_generations: Number of generations to run NSGA-2.\n",
    "    :return: (best_meta_fitness: 2-dim numpy array,\n",
    "              best_parameters: 2-dim numpy array)\n",
    "    \"\"\"\n",
    "\n",
    "    # Create the meta-problem-instance.\n",
    "    meta_problem = MetaProblem()\n",
    "\n",
    "    # Create an NSGA-2 Multi-Objective meta-optimizer.\n",
    "    optimizer = pg.algorithm(uda=pg.nsga2(gen=num_generations))\n",
    "    \n",
    "    # Create a population of candidate parameters for DE.\n",
    "    population = pg.population(prob=meta_problem, size=pop_size)\n",
    "    \n",
    "    # Perform meta-optimization of DE parameters.\n",
    "    population = optimizer.evolve(population)\n",
    "\n",
    "    # Get the best-found parameters and meta-fitnesses.\n",
    "    best_parameters = population.get_x()\n",
    "    best_meta_fitness = population.get_f()\n",
    "\n",
    "    # Sorted index for the meta-fitnesses.\n",
    "    idx_sort = np.argsort(best_meta_fitness[:, 0])\n",
    "\n",
    "    # Sort the best-found parameters and meta-fitnesses.\n",
    "    best_parameters = best_parameters[idx_sort]\n",
    "    best_meta_fitness = best_meta_fitness[idx_sort]\n",
    "    \n",
    "    return best_meta_fitness, best_parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Meta-Optimization\n",
    "\n",
    "We can now run the meta-optimization. This experiment took about an hour to run on my quad-core laptop PC. Depending on all the settings as well as the speed and number of CPU cores of your computer, this may take minutes, hours or days to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pop_size: 86\n",
      "CR: 0.38\n",
      "F: 0.28\n",
      "variant: 4 (best/2/exp)\n",
      "[7.07e+05 1.91e+04 8.57e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 73\n",
      "CR: 0.72\n",
      "F: 0.66\n",
      "variant: 4 (best/2/exp)\n",
      "[2.08e+06 3.14e+06 1.35e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.25\n",
      "F: 0.26\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[8.08e+05 3.09e+04 9.29e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 160\n",
      "CR: 0.98\n",
      "F: 0.13\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.19e+06 1.71e+06 5.31e+02 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 105\n",
      "CR: 0.08\n",
      "F: 0.46\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.58e+06 5.52e+06 1.75e+03 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.98\n",
      "F: 0.58\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.43e+06 2.40e+08 1.37e+03 4.76e+02 5.21e+02]\n",
      "\n",
      "pop_size: 38\n",
      "CR: 0.33\n",
      "F: 0.96\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.45e+06 1.57e+04 1.41e+03 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.91\n",
      "F: 0.11\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[8.24e+06 5.89e+07 2.76e+03 5.69e+02 5.20e+02]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.40\n",
      "F: 0.89\n",
      "variant: 9 (best/2/bin)\n",
      "[3.80e+06 2.20e+07 3.21e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.80\n",
      "F: 0.84\n",
      "variant: 7 (rand/1/bin)\n",
      "[3.28e+06 3.69e+07 1.54e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.41\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.97e+06 2.00e+02 3.25e+02 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 193\n",
      "CR: 0.21\n",
      "F: 0.94\n",
      "variant: 7 (rand/1/bin)\n",
      "[7.30e+06 4.16e+08 5.80e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 185\n",
      "CR: 0.15\n",
      "F: 0.55\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.84e+06 1.50e+08 4.18e+03 4.63e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[3.88e+07 2.25e+09 2.10e+04 7.96e+02 5.20e+02]\n",
      "\n",
      "pop_size: 106\n",
      "CR: 0.87\n",
      "F: 0.22\n",
      "variant: 6 (best/1/bin)\n",
      "[2.20e+07 4.53e+08 1.51e+04 5.68e+02 5.21e+02]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.33\n",
      "F: 0.73\n",
      "variant: 6 (best/1/bin)\n",
      "[3.69e+06 9.70e+06 3.56e+03 4.39e+02 5.20e+02]\n",
      "\n",
      "pop_size: 152\n",
      "CR: 0.44\n",
      "F: 0.36\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.07e+06 1.86e+07 2.69e+03 4.49e+02 5.20e+02]\n",
      "\n",
      "pop_size: 106\n",
      "CR: 0.38\n",
      "F: 0.39\n",
      "variant: 1 (best/1/exp)\n",
      "[8.73e+05 6.93e+04 1.21e+03 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 110\n",
      "CR: 0.42\n",
      "F: 0.05\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.87e+07 2.31e+09 1.23e+04 7.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 121\n",
      "CR: 0.02\n",
      "F: 0.23\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.52e+06 5.03e+06 2.32e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.69\n",
      "F: 0.30\n",
      "variant: 2 (rand/1/exp)\n",
      "[6.11e+05 1.08e+04 3.17e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 28\n",
      "CR: 0.37\n",
      "F: 0.98\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.39e+06 5.58e+05 8.29e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 67\n",
      "CR: 0.74\n",
      "F: 0.01\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.02e+07 2.82e+09 1.33e+04 9.77e+02 5.20e+02]\n",
      "\n",
      "pop_size: 156\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[6160.98 1632.46  300.    409.78  520.41]\n",
      "\n",
      "pop_size: 153\n",
      "CR: 0.84\n",
      "F: 0.62\n",
      "variant: 9 (best/2/bin)\n",
      "[2.17e+06 6.54e+07 1.49e+03 4.52e+02 5.21e+02]\n",
      "\n",
      "pop_size: 84\n",
      "CR: 0.45\n",
      "F: 0.55\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.17e+06 7.42e+06 2.42e+03 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 136\n",
      "CR: 0.14\n",
      "F: 0.30\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.25e+06 2.43e+06 2.01e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 143\n",
      "CR: 0.54\n",
      "F: 0.58\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.33e+06 5.05e+07 4.10e+03 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 164\n",
      "CR: 0.54\n",
      "F: 0.23\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.50e+06 1.89e+07 2.67e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 165\n",
      "CR: 0.58\n",
      "F: 0.56\n",
      "variant: 9 (best/2/bin)\n",
      "[3.02e+06 1.68e+07 3.54e+03 4.37e+02 5.21e+02]\n",
      "\n",
      "pop_size: 125\n",
      "CR: 0.06\n",
      "F: 0.58\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.90e+06 3.26e+07 1.79e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 99\n",
      "CR: 0.57\n",
      "F: 0.25\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.29e+06 3.47e+05 1.35e+03 4.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 134\n",
      "CR: 0.18\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[3.58e+06 4.44e+07 5.58e+03 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 130\n",
      "CR: 0.92\n",
      "F: 0.60\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.76e+06 2.51e+08 4.21e+03 4.77e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.49\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.34e+06 6.54e+05 1.94e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 157\n",
      "CR: 0.06\n",
      "F: 0.55\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.20e+06 4.22e+07 2.63e+03 4.51e+02 5.20e+02]\n",
      "\n",
      "pop_size: 118\n",
      "CR: 0.80\n",
      "F: 0.21\n",
      "variant: 10 (rand/2/bin)\n",
      "[174436.8  124514.49    330.59    411.56    520.53]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.66\n",
      "F: 0.23\n",
      "variant: 4 (best/2/exp)\n",
      "[55558.02   581.19   300.01   418.73   520.18]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.47\n",
      "F: 0.70\n",
      "variant: 2 (rand/1/exp)\n",
      "[6.85e+05 6.87e+02 3.91e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 146\n",
      "CR: 0.40\n",
      "F: 0.56\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2.66e+06 7.54e+06 3.13e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 134\n",
      "CR: 0.15\n",
      "F: 0.68\n",
      "variant: 7 (rand/1/bin)\n",
      "[4.96e+06 4.19e+07 3.55e+03 4.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.44\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[4.62e+05 5.05e+02 3.47e+02 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 157\n",
      "CR: 0.08\n",
      "F: 0.69\n",
      "variant: 7 (rand/1/bin)\n",
      "[3.82e+06 7.27e+07 3.90e+03 4.64e+02 5.20e+02]\n",
      "\n",
      "pop_size: 118\n",
      "CR: 0.78\n",
      "F: 0.18\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.32e+05 7.97e+04 3.31e+02 4.21e+02 5.21e+02]\n",
      "\n",
      "pop_size: 28\n",
      "CR: 0.37\n",
      "F: 0.41\n",
      "variant: 7 (rand/1/bin)\n",
      "[3.48e+05 4.93e+02 3.01e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 156\n",
      "CR: 0.90\n",
      "F: 0.96\n",
      "variant: 9 (best/2/bin)\n",
      "[1.76e+07 2.11e+09 8.27e+03 5.57e+02 5.21e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.98\n",
      "F: 0.13\n",
      "variant: 4 (best/2/exp)\n",
      "[3.71e+07 4.55e+09 3.84e+04 9.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 161\n",
      "CR: 0.66\n",
      "F: 0.23\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.41e+06 1.35e+07 2.03e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 106\n",
      "CR: 0.37\n",
      "F: 0.39\n",
      "variant: 1 (best/1/exp)\n",
      "[1.23e+06 9.12e+04 1.20e+03 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.08e+06 8.69e+05 1.72e+03 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 115\n",
      "CR: 0.25\n",
      "F: 0.26\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.38e+06 2.14e+07 1.65e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.80\n",
      "F: 0.21\n",
      "variant: 9 (best/2/bin)\n",
      "[1.51e+07 1.53e+08 1.08e+04 4.49e+02 5.21e+02]\n",
      "\n",
      "pop_size: 106\n",
      "CR: 0.39\n",
      "F: 0.55\n",
      "variant: 1 (best/1/exp)\n",
      "[1.34e+06 4.90e+05 2.92e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 165\n",
      "CR: 0.56\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[7.78e+05 3.70e+04 5.17e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.33\n",
      "F: 0.30\n",
      "variant: 6 (best/1/bin)\n",
      "[198114.82   1091.35    409.35    411.95    520.37]\n",
      "\n",
      "pop_size: 63\n",
      "CR: 0.69\n",
      "F: 0.73\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.19e+06 1.43e+06 9.29e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 98\n",
      "CR: 0.58\n",
      "F: 0.94\n",
      "variant: 9 (best/2/bin)\n",
      "[9.94e+06 5.09e+08 8.73e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.38\n",
      "F: 0.21\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.15e+06 2.99e+03 9.59e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 164\n",
      "CR: 0.83\n",
      "F: 0.94\n",
      "variant: 9 (best/2/bin)\n",
      "[1.26e+07 1.40e+09 9.71e+03 5.39e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.33\n",
      "F: 0.05\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.01e+07 9.66e+08 3.62e+03 5.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.48\n",
      "F: 0.58\n",
      "variant: 2 (rand/1/exp)\n",
      "[322130.99    523.39    338.37    405.02    520.14]\n",
      "\n",
      "pop_size: 127\n",
      "CR: 0.05\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.10e+06 6.76e+07 2.17e+03 4.71e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[66303.07   200.57   300.     403.34   520.13]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.45\n",
      "F: 0.23\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.36e+06 6.82e+04 6.94e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 79\n",
      "CR: 0.41\n",
      "F: 0.66\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.89e+06 6.61e+06 3.31e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.45\n",
      "F: 0.52\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.54e+05 2.00e+02 3.41e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 143\n",
      "CR: 0.54\n",
      "F: 0.56\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.91e+06 4.37e+06 1.62e+03 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 146\n",
      "CR: 0.40\n",
      "F: 0.58\n",
      "variant: 2 (rand/1/exp)\n",
      "[4.24e+06 2.92e+07 2.14e+03 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 84\n",
      "CR: 0.00\n",
      "F: 0.05\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.94e+06 2.29e+05 3.47e+03 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 123\n",
      "CR: 0.43\n",
      "F: 0.28\n",
      "variant: 4 (best/2/exp)\n",
      "[7.03e+05 1.47e+05 1.10e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 121\n",
      "CR: 0.04\n",
      "F: 0.27\n",
      "variant: 4 (best/2/exp)\n",
      "[1.16e+06 1.00e+06 1.09e+03 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.40\n",
      "F: 0.05\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.58e+06 3.64e+05 2.16e+03 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 85\n",
      "CR: 0.00\n",
      "F: 0.05\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.01e+06 5.53e+05 3.56e+03 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.33\n",
      "F: 0.30\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.20e+06 4.98e+07 5.14e+03 4.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[3.18e+06 5.79e+07 2.70e+03 4.34e+02 5.18e+02]\n",
      "\n",
      "pop_size: 84\n",
      "CR: 0.45\n",
      "F: 0.48\n",
      "variant: 10 (rand/2/bin)\n",
      "[3.45e+06 1.04e+07 3.79e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.83\n",
      "F: 0.10\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.17e+06 4.35e+06 7.31e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 155\n",
      "CR: 0.88\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[4.25e+07 3.56e+09 2.24e+04 1.02e+03 5.20e+02]\n",
      "\n",
      "pop_size: 177\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1356.97 1057.01  300.01  418.49  520.54]\n",
      "\n",
      "pop_size: 38\n",
      "CR: 0.42\n",
      "F: 0.96\n",
      "variant: 2 (rand/1/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.55e+06 7.46e+04 1.77e+03 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[42526.6    203.17   300.     423.73   519.06]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.46\n",
      "F: 0.25\n",
      "variant: 4 (best/2/exp)\n",
      "[384535.55   1126.93   1066.65    427.06    520.22]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.65\n",
      "F: 0.23\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[9.58e+05 1.69e+05 5.51e+02 4.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[623.47 200.02 300.   408.7  520.34]\n",
      "\n",
      "pop_size: 155\n",
      "CR: 0.66\n",
      "F: 0.23\n",
      "variant: 9 (best/2/bin)\n",
      "[52096.15  3293.66   303.23   433.79   520.52]\n",
      "\n",
      "pop_size: 127\n",
      "CR: 0.06\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.15e+06 8.91e+07 4.21e+03 4.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 160\n",
      "CR: 0.98\n",
      "F: 0.09\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.56e+06 1.44e+07 2.96e+03 4.65e+02 5.20e+02]\n",
      "\n",
      "pop_size: 156\n",
      "CR: 0.90\n",
      "F: 0.44\n",
      "variant: 9 (best/2/bin)\n",
      "[1893.33 1543.63  300.08  425.33  520.45]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.45\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[3.52e+05 1.66e+03 3.19e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 73\n",
      "CR: 0.38\n",
      "F: 0.28\n",
      "variant: 4 (best/2/exp)\n",
      "[6.85e+05 8.31e+03 1.12e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 22\n",
      "CR: 0.41\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[7.35e+05 9.07e+02 3.03e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 88\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.33e+06 1.06e+06 3.13e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 164\n",
      "CR: 0.56\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6.45e+05 3.36e+04 5.18e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.48\n",
      "F: 0.58\n",
      "variant: 9 (best/2/bin)\n",
      "[91636.75   200.02   300.     434.78   520.44]\n",
      "\n",
      "pop_size: 165\n",
      "CR: 0.56\n",
      "F: 0.40\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.78e+06 2.54e+07 2.49e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.69\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.36e+06 2.20e+05 5.03e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 125\n",
      "CR: 0.06\n",
      "F: 0.31\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.06e+06 1.06e+07 2.18e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 28\n",
      "CR: 0.49\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[6.01e+05 7.57e+02 3.33e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.37\n",
      "F: 0.42\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.12e+06 4.35e+05 1.13e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.48\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[7.01e+05 4.69e+02 3.67e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[30700.17   200.2    566.5    408.79   520.16]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[57434.1    200.19   300.     409.9    520.15]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.69\n",
      "F: 0.33\n",
      "variant: 2 (rand/1/exp)\n",
      "[6.55e+05 1.45e+04 3.21e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.44\n",
      "F: 0.04\n",
      "variant: 9 (best/2/bin)\n",
      "[1.12e+07 2.96e+08 4.66e+03 5.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[28312.18   200.     300.     409.78   520.21]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.06\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.93e+08 1.24e+10 7.27e+04 2.40e+03 5.20e+02]\n",
      "\n",
      "pop_size: 178\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.54e+06 1.62e+08 6.65e+03 4.61e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[1.03e+09 2.16e+10 1.40e+08 1.16e+04 5.21e+02]\n",
      "\n",
      "pop_size: 187\n",
      "CR: 0.41\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[8.27e+06 1.15e+08 8.93e+03 4.56e+02 5.20e+02]\n",
      "\n",
      "pop_size: 179\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[1.69e+07 5.36e+08 1.45e+04 4.77e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.47\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[68069.01   200.01   300.     418.49   520.31]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[41460.99   200.01   300.     427.17   520.2 ]\n",
      "\n",
      "pop_size: 26\n",
      "CR: 0.49\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.15e+06 2.69e+03 1.26e+03 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.41\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[4.20e+06 4.95e+06 3.59e+03 4.39e+02 5.20e+02]\n",
      "\n",
      "pop_size: 20\n",
      "CR: 0.00\n",
      "F: 0.05\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.89e+05 1.16e+07 1.36e+03 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1026.34 3284.75  300.01  427.19  520.42]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2625.9   200.    300.    426.09  520.33]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.50\n",
      "F: 0.30\n",
      "variant: 4 (best/2/exp)\n",
      "[1.03e+06 9.21e+05 2.62e+03 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 28\n",
      "CR: 0.31\n",
      "F: 0.52\n",
      "variant: 6 (best/1/bin)\n",
      "[47358.37   200.05   300.     417.58   520.32]\n",
      "\n",
      "pop_size: 174\n",
      "CR: 0.34\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.60e+06 4.21e+06 2.78e+03 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.82\n",
      "F: 0.54\n",
      "variant: 7 (rand/1/bin)\n",
      "[7.96e+05 4.32e+08 7.39e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.47\n",
      "F: 0.58\n",
      "variant: 9 (best/2/bin)\n",
      "[107655.46    200.03    300.      426.15    520.38]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.48\n",
      "F: 0.70\n",
      "variant: 2 (rand/1/exp)\n",
      "[5.96e+05 1.32e+03 3.15e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 28\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.27e+07 2.15e+08 7.48e+03 5.55e+02 5.21e+02]\n",
      "\n",
      "pop_size: 151\n",
      "CR: 0.36\n",
      "F: 0.44\n",
      "variant: 9 (best/2/bin)\n",
      "[1.61e+06 9.72e+05 2.17e+03 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.32\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[49838.17   970.97  2538.53   422.25   520.27]\n",
      "\n",
      "pop_size: 168\n",
      "CR: 0.46\n",
      "F: 0.30\n",
      "variant: 4 (best/2/exp)\n",
      "[1.60e+06 1.61e+06 3.11e+03 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.32\n",
      "F: 0.64\n",
      "variant: 6 (best/1/bin)\n",
      "[6908.63  200.    300.    411.98  520.26]\n",
      "\n",
      "pop_size: 164\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1.37e+06 1.07e+07 1.76e+03 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 25\n",
      "CR: 0.83\n",
      "F: 0.13\n",
      "variant: 9 (best/2/bin)\n",
      "[5.16e+07 4.46e+09 2.45e+04 7.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.45\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[4.98e+05 6.84e+02 3.99e+02 4.05e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.71\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.85e+05 1.03e+03 3.60e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.05\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+08 5.29e+09 3.14e+04 1.72e+03 5.20e+02]\n",
      "\n",
      "pop_size: 20\n",
      "CR: 0.00\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.23e+06 7.38e+03 2.19e+03 4.06e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.50\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.88e+06 1.06e+07 4.25e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.31\n",
      "variant: 4 (best/2/exp)\n",
      "[12623.73  2259.01   639.15   420.11   520.28]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.30\n",
      "variant: 4 (best/2/exp)\n",
      "[73237.54  1020.29   300.43   418.49   520.31]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.51\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.17e+06 4.07e+06 2.49e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 7 (rand/1/bin)\n",
      "[103279.2     200.02    300.      402.85    520.24]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.01\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.55e+06 1.23e+08 5.64e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[89003.7    202.69   300.     410.89   520.14]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.89\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2687.27  200.    300.    409.78  520.32]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[1.22e+07 6.26e+08 8.93e+03 4.65e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.13  200.    300.    434.78  520.53]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.26\n",
      "F: 0.64\n",
      "variant: 6 (best/1/bin)\n",
      "[30750.59   200.01   300.     403.36   520.19]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.48\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.07e+05 1.13e+03 4.59e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 178\n",
      "CR: 0.83\n",
      "F: 0.06\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+07 1.83e+09 2.18e+04 8.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.80\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[558.6  200.   300.18 434.78 520.54]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.50\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.82e+06 2.95e+06 2.63e+03 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.71\n",
      "F: 0.22\n",
      "variant: 4 (best/2/exp)\n",
      "[62290.92   268.31   300.19   418.61   520.23]\n",
      "\n",
      "pop_size: 20\n",
      "CR: 0.00\n",
      "F: 0.06\n",
      "variant: 5 (rand/2/exp)\n",
      "[8.27e+05 8.48e+06 3.04e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1454.68  200.02  300.    419.56  520.29]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.41\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[9.39e+05 1.12e+07 7.63e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.45\n",
      "F: 0.59\n",
      "variant: 4 (best/2/exp)\n",
      "[303796.62    541.57    304.76    417.71    520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[176578.65    255.82    349.01    409.99    520.1 ]\n",
      "\n",
      "pop_size: 88\n",
      "CR: 0.32\n",
      "F: 0.63\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.87e+06 5.74e+06 2.80e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.80\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[2317.33  200.    300.    434.78  520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[9.39e+05 9.21e+02 1.28e+03 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 7 (rand/1/bin)\n",
      "[3.50e+05 1.71e+07 1.23e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.72\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[54832.5    200.     300.     418.47   520.15]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.27\n",
      "F: 0.63\n",
      "variant: 6 (best/1/bin)\n",
      "[1.58e+06 1.44e+08 4.44e+03 4.79e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[1429.52  200.    300.    427.17  520.56]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.46\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[190741.86    355.04    309.91    401.44    520.08]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 2 (rand/1/exp)\n",
      "[40537.65   200.25   300.     401.34   520.18]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.51\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[7378.88  200.    302.91  427.17  520.42]\n",
      "\n",
      "pop_size: 92\n",
      "CR: 0.78\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[7.23e+05 4.50e+05 4.38e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.83\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[48039.31  3376.44  6217.9    426.09   520.47]\n",
      "\n",
      "pop_size: 187\n",
      "CR: 0.83\n",
      "F: 0.04\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.09e+06 1.19e+07 1.81e+03 4.20e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.30\n",
      "F: 0.64\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[33599.58   200.05   300.71   409.38   520.26]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[40149.28   200.04   300.     400.03   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.77e+05 6.55e+02 4.85e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[530.37 200.   300.   418.47 520.34]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.46\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[293033.74    639.2     315.94    404.2     520.11]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[37778.51   200.04   300.     401.16   520.14]\n",
      "\n",
      "pop_size: 86\n",
      "CR: 0.48\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1.07e+06 1.08e+06 1.67e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.50\n",
      "F: 0.70\n",
      "variant: 2 (rand/1/exp)\n",
      "[4.11e+05 3.30e+02 3.15e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.48\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.40e+05 1.83e+03 5.22e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.50\n",
      "F: 0.30\n",
      "variant: 4 (best/2/exp)\n",
      "[1.42e+06 1.33e+06 2.07e+03 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.32\n",
      "F: 0.65\n",
      "variant: 6 (best/1/bin)\n",
      "[26179.55   200.     300.     412.     520.26]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1606.39  200.25  300.    418.47  519.83]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.64e+06 7.30e+07 3.96e+03 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[1.02e+08 9.08e+09 3.12e+04 1.61e+03 5.20e+02]\n",
      "\n",
      "pop_size: 82\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1.18e+06 4.82e+05 1.45e+03 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.67\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[71617.39   201.08   300.     403.84   520.14]\n",
      "\n",
      "pop_size: 19\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.64e+05 6.64e+02 3.14e+02 4.05e+02 5.20e+02]\n",
      "\n",
      "pop_size: 77\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.98e+06 3.73e+06 2.19e+03 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.70\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.47e+06 6.49e+06 1.86e+03 4.31e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6013.49  202.76  300.    411.95  518.38]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.72e+05 2.18e+02 3.00e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[2213.09  200.    380.98  400.11  520.5 ]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.34\n",
      "F: 0.64\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[20057.77   200.     300.     401.37   520.33]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.81\n",
      "F: 0.64\n",
      "variant: 6 (best/1/bin)\n",
      "[2.26e+06 1.92e+07 1.37e+03 4.47e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.31\n",
      "F: 0.68\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.87e+05 2.00e+02 3.01e+02 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.54e+07 2.55e+08 5.12e+03 7.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.67\n",
      "F: 0.57\n",
      "variant: 4 (best/2/exp)\n",
      "[9.95e+05 1.47e+05 4.67e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.80\n",
      "F: 0.62\n",
      "variant: 4 (best/2/exp)\n",
      "[5728.81  200.    300.    410.86  520.18]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1115.49  200.    300.01  418.59  520.48]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.29\n",
      "variant: 4 (best/2/exp)\n",
      "[173285.75   8824.26    340.28    418.7     520.3 ]\n",
      "\n",
      "pop_size: 85\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.46e+06 1.18e+06 2.37e+03 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 84\n",
      "CR: 0.48\n",
      "F: 0.68\n",
      "variant: 7 (rand/1/bin)\n",
      "[4.53e+06 7.41e+06 3.01e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.33\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[285477.62    865.33    768.77    403.97    520.11]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.62\n",
      "variant: 4 (best/2/exp)\n",
      "[2407.89  366.46  372.74  402.31  520.21]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.32\n",
      "F: 0.42\n",
      "variant: 7 (rand/1/bin)\n",
      "[9.88e+05 2.34e+04 4.22e+02 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.06\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.04e+06 1.35e+06 6.35e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.80\n",
      "F: 0.61\n",
      "variant: 9 (best/2/bin)\n",
      "[7672.84  200.    300.    418.48  520.5 ]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[663.61 366.19 300.   409.78 520.3 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.50\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[205757.16   5233.86    492.93    402.36    520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[49219.74   200.     300.     419.13   520.19]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.57\n",
      "variant: 1 (best/1/exp)\n",
      "[50246.07  3104.82   417.62   430.98   520.18]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.59e+08 5.16e+09 3.36e+04 9.21e+02 5.21e+02]\n",
      "\n",
      "pop_size: 166\n",
      "CR: 0.45\n",
      "F: 0.59\n",
      "variant: 4 (best/2/exp)\n",
      "[2.76e+06 6.58e+07 4.44e+03 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.32\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[242882.33   1891.15    934.44    418.04    520.11]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.46\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[7.44e+05 1.44e+03 3.97e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[19189.21   200.01   300.     417.53   520.26]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[684.25 200.01 300.   434.78 520.31]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[14066.88   657.4    873.49   426.13   520.18]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.47\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[8.41e+05 1.59e+04 7.58e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1845.82  200.    306.95  418.48  520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.58\n",
      "variant: 2 (rand/1/exp)\n",
      "[6.69e+05 2.23e+02 3.07e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.83\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[2173.81  200.34  300.    426.09  520.29]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.91\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[8.16e+06 1.31e+08 6.34e+03 4.45e+02 5.18e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[106767.09    231.4     300.06    426.08    520.43]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.82\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[1096.22  200.    300.    426.09  520.19]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.92\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[361.51 200.01 300.   418.47 520.35]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.12e+06 1.08e+07 3.84e+03 4.33e+02 5.18e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.57\n",
      "variant: 9 (best/2/bin)\n",
      "[1521.76  200.    302.48  418.47  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.45\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[318686.04    803.2     344.39    400.67    520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.31\n",
      "F: 0.70\n",
      "variant: 7 (rand/1/bin)\n",
      "[107881.21    252.48    301.98    418.4     520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[23944.     200.04   300.     402.17   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10672.3    200.    1044.07   417.39   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[30644.79   200.8    300.     418.48   520.45]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.89\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[4.02e+05 2.76e+06 4.05e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.43\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.53e+05 3.22e+02 3.26e+02 4.10e+02 5.19e+02]\n",
      "\n",
      "pop_size: 169\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.90e+06 3.15e+07 2.63e+03 4.35e+02 5.18e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.51\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[148881.93    200.07    300.      426.1     520.37]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.34e+06 7.46e+06 4.75e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[1.84e+06 8.48e+06 5.13e+02 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[1.37e+06 2.04e+03 9.57e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.45\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[203336.07    713.18    494.99    404.68    520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.88\n",
      "variant: 4 (best/2/exp)\n",
      "[5637.43  260.36  300.    409.62  520.16]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.92\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.11e+07 2.47e+09 2.47e+04 7.88e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.84\n",
      "F: 0.42\n",
      "variant: 10 (rand/2/bin)\n",
      "[139443.37 113844.6     309.57    410.33    520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.49\n",
      "F: 0.42\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.34e+06 3.24e+08 4.28e+03 4.58e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.30\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n",
      "[9.56e+05 1.81e+03 1.92e+03 4.06e+02 5.20e+02]\n",
      "\n",
      "pop_size: 171\n",
      "CR: 0.84\n",
      "F: 0.04\n",
      "variant: 7 (rand/1/bin)\n",
      "[3.96e+06 3.22e+07 1.95e+03 4.24e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.31\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.46e+05 3.22e+02 3.01e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[210.06 200.   300.   418.47 520.41]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[2665.86  200.    300.12  411.95  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.63\n",
      "variant: 4 (best/2/exp)\n",
      "[18890.97   200.     300.     410.86   520.16]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.26e+06 6.24e+08 6.46e+03 5.49e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.46\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[3.71e+05 2.88e+02 3.11e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.32\n",
      "F: 0.67\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.68e+05 6.58e+03 2.05e+03 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1228.06  200.05  300.    418.48  520.32]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.92\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1652.74  200.01  300.    434.78  520.41]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.44\n",
      "variant: 7 (rand/1/bin)\n",
      "[8.97e+06 2.02e+08 1.08e+04 5.88e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.34\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[9.70e+05 1.93e+05 1.88e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.90\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[3.09e+05 1.19e+06 3.51e+02 4.37e+02 5.20e+02]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.45\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2.79e+05 1.62e+06 2.75e+03 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.61e+06 3.51e+07 3.45e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.20e+08 8.17e+09 3.05e+04 1.51e+03 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[2.34e+07 1.88e+09 1.55e+04 7.87e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[230.17 200.   300.   434.78 520.35]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.30e+06 3.34e+07 2.82e+03 4.26e+02 5.18e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.58\n",
      "variant: 4 (best/2/exp)\n",
      "[8178.95  200.    300.    434.78  520.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.69\n",
      "variant: 2 (rand/1/exp)\n",
      "[35815.61   205.4    311.15   408.75   520.15]\n",
      "\n",
      "pop_size: 163\n",
      "CR: 0.85\n",
      "F: 0.71\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.22e+06 2.75e+08 4.33e+03 4.63e+02 5.20e+02]\n",
      "\n",
      "pop_size: 18\n",
      "CR: 0.29\n",
      "F: 0.02\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.15e+06 1.24e+09 5.50e+03 5.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[5.37e+06 5.52e+07 4.45e+03 4.51e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.01\n",
      "variant: 4 (best/2/exp)\n",
      "[1.45e+08 6.04e+09 3.76e+04 1.87e+03 5.20e+02]\n",
      "\n",
      "pop_size: 67\n",
      "CR: 0.90\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.24e+07 9.54e+08 7.05e+03 4.97e+02 5.21e+02]\n",
      "\n",
      "pop_size: 158\n",
      "CR: 0.84\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[117573.83  59105.1     319.97    418.04    520.38]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[3.45e+07 1.48e+09 1.94e+04 6.68e+02 5.20e+02]\n",
      "\n",
      "pop_size: 165\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 2 (rand/1/exp)\n",
      "[4.92e+05 8.24e+06 6.38e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 151\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.85e+06 1.55e+08 4.78e+03 4.71e+02 5.20e+02]\n",
      "\n",
      "pop_size: 32\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[1.40e+08 6.84e+09 5.07e+04 1.89e+03 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.36\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[1.86e+08 8.73e+09 3.39e+04 2.71e+03 5.20e+02]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.77\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.62e+06 3.25e+08 9.66e+03 4.74e+02 5.20e+02]\n",
      "\n",
      "pop_size: 169\n",
      "CR: 0.83\n",
      "F: 0.05\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.66e+06 2.87e+06 2.27e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[25181.4    200.     304.4    417.39   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n",
      "[286053.5    1501.79   2188.34    403.89    520.1 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.18   300.     407.62   520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[30859.98   200.17   300.37   427.17   520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.32\n",
      "F: 0.65\n",
      "variant: 6 (best/1/bin)\n",
      "[28187.01   200.     300.15   418.67   520.21]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[1.10e+06 7.87e+07 2.80e+03 4.29e+02 5.17e+02]\n",
      "\n",
      "pop_size: 169\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.34e+06 1.24e+07 2.51e+03 4.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.46\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[24723.25   200.     300.     426.12   520.4 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.62\n",
      "variant: 4 (best/2/exp)\n",
      "[855.15 200.04 312.25 426.13 520.26]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1236.63  200.    322.3   409.91  520.55]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6367.58  767.75 2413.95  435.44  520.48]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.67\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[28408.25   200.25   300.     426.09   520.01]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.30\n",
      "F: 0.63\n",
      "variant: 5 (rand/2/exp)\n",
      "[7.06e+05 8.66e+02 1.18e+03 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1163.77  200.03  300.    434.78  515.29]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.67\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[54907.93   200.09   300.     417.42   520.54]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[7545.88  200.02  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[4.74e+06 6.34e+08 1.12e+04 4.73e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.82\n",
      "F: 0.65\n",
      "variant: 4 (best/2/exp)\n",
      "[1.09e+06 7.71e+05 3.82e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[63377.02   202.71   300.     400.89   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.30\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[164375.71    200.82    300.      409.1     520.25]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1606.39  200.25  300.    418.47  519.83]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.83\n",
      "F: 0.67\n",
      "variant: 10 (rand/2/bin)\n",
      "[26829.49   470.75   300.01   417.53   520.47]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.83\n",
      "F: 0.02\n",
      "variant: 9 (best/2/bin)\n",
      "[3.00e+07 2.57e+09 2.14e+04 8.70e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.42e+08 8.92e+09 1.13e+04 3.49e+03 5.20e+02]\n",
      "\n",
      "pop_size: 177\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[7.33e+05 6.63e+06 1.08e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 34\n",
      "CR: 0.83\n",
      "F: 0.74\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.06e+06 6.93e+07 1.82e+03 4.66e+02 5.20e+02]\n",
      "\n",
      "pop_size: 186\n",
      "CR: 0.83\n",
      "F: 0.00\n",
      "variant: 9 (best/2/bin)\n",
      "[5.44e+07 4.09e+09 3.04e+04 9.82e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.65\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.14e+07 6.83e+08 6.54e+03 5.28e+02 5.21e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[8.47e+08 1.68e+10 6.62e+04 5.83e+03 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.47\n",
      "F: 0.58\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.32e+05 5.22e+02 3.44e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.42\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.11e+06 1.22e+07 7.07e+02 5.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[237946.03    503.      307.02    404.58    520.11]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.46\n",
      "variant: 5 (rand/2/exp)\n",
      "[8137.87  200.    408.8   418.55  520.14]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.71\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.25e+07 1.08e+09 7.80e+03 5.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.31\n",
      "F: 0.02\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.61e+07 8.19e+09 1.34e+04 1.98e+03 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.24e+05 9.00e+02 5.79e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[3.78e+05 2.86e+06 4.07e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.32\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[2.36e+05 2.08e+02 3.00e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.65\n",
      "variant: 6 (best/1/bin)\n",
      "[6.93e+04 1.59e+08 3.70e+02 4.53e+02 5.21e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.67\n",
      "F: 0.50\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.65e+06 1.51e+08 5.50e+03 4.64e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.84\n",
      "F: 0.11\n",
      "variant: 2 (rand/1/exp)\n",
      "[4.28e+07 6.93e+09 2.01e+04 1.24e+03 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 177\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[9.79e+05 2.29e+07 2.16e+03 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.73\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7149.02  200.    355.78  409.95  520.15]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.48e+06 1.31e+07 2.85e+03 4.22e+02 5.18e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.32\n",
      "F: 0.65\n",
      "variant: 6 (best/1/bin)\n",
      "[26179.55   200.     300.     412.     520.26]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.84\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[796.52 202.22 300.   410.86 520.28]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.90\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[6.14e+06 1.23e+08 2.83e+03 4.64e+02 5.16e+02]\n",
      "\n",
      "pop_size: 174\n",
      "CR: 0.83\n",
      "F: 0.02\n",
      "variant: 4 (best/2/exp)\n",
      "[2.19e+07 9.53e+08 2.01e+04 5.37e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 7 (rand/1/bin)\n",
      "[6823.72  209.49  300.    417.45  520.54]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.02\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.00e+06 5.30e+07 4.18e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[5943.17  200.    300.    408.71  520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[362.18 200.04 793.68 426.09 520.59]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.70\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[48194.9    208.77   300.     427.4    520.15]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[2.87e+06 1.25e+08 2.84e+03 4.63e+02 5.20e+02]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[8.19e+07 4.97e+09 6.46e+04 2.33e+03 5.20e+02]\n",
      "\n",
      "pop_size: 25\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.20e+07 2.76e+09 2.09e+04 8.89e+02 5.20e+02]\n",
      "\n",
      "pop_size: 160\n",
      "CR: 0.48\n",
      "F: 0.43\n",
      "variant: 5 (rand/2/exp)\n",
      "[5.38e+06 9.16e+07 2.93e+03 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 182\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2.16e+06 6.81e+06 1.79e+03 4.37e+02 5.21e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[6.58e+08 1.17e+10 7.13e+07 9.71e+03 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.01\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.44e+07 1.09e+09 2.02e+04 5.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.84\n",
      "F: 0.43\n",
      "variant: 7 (rand/1/bin)\n",
      "[9.88e+05 1.44e+07 9.27e+02 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.35e+06 1.02e+06 2.11e+03 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.45\n",
      "variant: 4 (best/2/exp)\n",
      "[1.02e+06 2.77e+05 1.64e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1340.18  200.    300.    417.39  520.29]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.52\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[183430.56    233.23    596.83    427.42    520.11]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[7.70e+04 4.84e+06 3.51e+02 4.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 45\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.30e+06 3.48e+05 5.78e+02 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.20e+06 4.29e+07 1.32e+03 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.12e+06 1.08e+07 3.84e+03 4.33e+02 5.18e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.50\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[176578.65    255.82    349.01    409.99    520.1 ]\n",
      "\n",
      "pop_size: 184\n",
      "CR: 0.31\n",
      "F: 0.05\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.62e+06 8.57e+06 3.45e+03 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.64\n",
      "variant: 6 (best/1/bin)\n",
      "[5.00e+06 1.24e+09 2.20e+04 6.07e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.57\n",
      "variant: 10 (rand/2/bin)\n",
      "[24592.35   200.01   300.02   434.78   520.5 ]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 2 (rand/1/exp)\n",
      "[34548.07   640.06   301.54   426.09   520.17]\n",
      "\n",
      "pop_size: 167\n",
      "CR: 0.30\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n",
      "[4.67e+06 2.39e+08 6.23e+03 4.80e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.64\n",
      "F: 0.61\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.90e+06 1.26e+05 5.15e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.94\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[1.03e+07 3.96e+09 2.49e+04 1.19e+03 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.48e+06 3.83e+06 1.87e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.85\n",
      "F: 0.03\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.87e+08 8.75e+09 1.03e+04 3.20e+03 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.32\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[9.45e+05 7.60e+03 1.36e+03 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.89\n",
      "F: 0.65\n",
      "variant: 6 (best/1/bin)\n",
      "[1.14e+06 9.34e+08 1.06e+04 4.53e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[729.85 200.   300.   418.47 520.28]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.06e+06 4.93e+02 3.07e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.85\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6.73e+06 1.41e+08 4.42e+03 5.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.38\n",
      "F: 0.68\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.01e+06 1.40e+06 3.29e+03 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.65\n",
      "variant: 9 (best/2/bin)\n",
      "[371.56 200.   300.   410.86 520.52]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.39e+06 7.25e+06 2.14e+03 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[102724.7     200.01    300.01    412.49    520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[4950.94  201.73  561.16  427.17  520.57]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.30\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n",
      "[7.88e+05 8.28e+02 1.76e+03 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.63\n",
      "variant: 9 (best/2/bin)\n",
      "[3384.62  200.    300.    426.09  520.49]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1076.69  200.    303.33  426.2   520.46]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.31\n",
      "F: 0.74\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.58e+06 1.97e+08 5.10e+03 4.80e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.85\n",
      "F: 0.01\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.57e+09 1.25e+10 7.45e+07 7.42e+03 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.30\n",
      "F: 0.74\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.47e+05 1.34e+03 1.29e+03 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.01\n",
      "variant: 10 (rand/2/bin)\n",
      "[6.30e+06 1.86e+08 4.47e+03 4.55e+02 5.21e+02]\n",
      "\n",
      "pop_size: 171\n",
      "CR: 0.83\n",
      "F: 0.43\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.94e+06 2.23e+07 2.17e+03 4.39e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.02\n",
      "variant: 4 (best/2/exp)\n",
      "[1.11e+08 4.49e+09 3.54e+04 1.21e+03 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.32\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[8.53e+05 1.42e+03 4.87e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[36262.04 51570.13   301.48   402.37   520.33]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.50\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[3.87e+05 3.40e+02 3.05e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.32\n",
      "F: 0.53\n",
      "variant: 7 (rand/1/bin)\n",
      "[7.20e+05 1.32e+03 1.51e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.98e+06 7.21e+05 1.50e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.29e+07 2.64e+09 1.33e+04 8.73e+02 5.20e+02]\n",
      "\n",
      "pop_size: 171\n",
      "CR: 0.90\n",
      "F: 0.04\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.96e+06 1.61e+08 5.30e+03 4.43e+02 5.19e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.81\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[15261.61   200.58   300.     417.39   520.19]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.00\n",
      "variant: 2 (rand/1/exp)\n",
      "[9.10e+07 8.87e+09 1.13e+07 1.84e+03 5.21e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.70\n",
      "F: 0.80\n",
      "variant: 4 (best/2/exp)\n",
      "[6.66e+06 2.02e+08 8.34e+03 4.73e+02 5.20e+02]\n",
      "\n",
      "pop_size: 174\n",
      "CR: 0.76\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[1.77e+06 3.40e+07 1.30e+03 4.43e+02 5.21e+02]\n",
      "\n",
      "pop_size: 171\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.39e+06 1.51e+07 2.54e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.80\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1409.61  202.46  300.    408.7   520.27]\n",
      "\n",
      "pop_size: 171\n",
      "CR: 0.91\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.14e+06 7.82e+07 4.70e+03 4.60e+02 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.58\n",
      "variant: 10 (rand/2/bin)\n",
      "[8.25e+06 4.56e+08 5.06e+03 4.90e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.96e+07 4.99e+09 1.51e+04 1.04e+03 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.02\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.72e+07 4.32e+09 1.91e+04 1.19e+03 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3669.48  200.52  300.    427.17  520.35]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.93\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2894.39 4016.95  446.66  418.61  520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.29\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[7.04e+05 2.87e+03 8.53e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.30e+06 3.34e+07 2.82e+03 4.26e+02 5.18e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[66743.75   200.23   300.     409.83   520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.93\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[1.06e+07 1.88e+09 3.34e+04 5.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[7.32e+05 4.96e+04 3.29e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.01e+07 1.06e+09 9.02e+03 7.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.24\n",
      "F: 0.69\n",
      "variant: 5 (rand/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.99e+05 4.76e+03 2.02e+03 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.94\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 1477.2  14393.82   300.23   417.5    520.45]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.31\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[44144.42   200.01   300.68   418.57   520.25]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.2    517.75]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[8.85e+06 2.43e+08 8.33e+03 4.68e+02 5.20e+02]\n",
      "\n",
      "pop_size: 167\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[17549.14 17393.74   302.29   418.98   520.38]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[2.23e+07 6.23e+08 1.57e+04 5.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.48\n",
      "F: 0.67\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.54e+05 2.18e+02 3.00e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[39145.31   200.01   300.     418.47   519.97]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.96e+06 6.45e+06 1.63e+03 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[7.50e+07 6.06e+09 2.45e+04 1.23e+03 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.52\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[77911.22   223.19   671.1    409.39   520.13]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.86\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1022.01  200.01  300.    410.86  520.29]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 5 (rand/2/exp)\n",
      "[71113.22  2119.69   300.06   400.98   520.22]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[1429.52  200.    300.    427.17  520.56]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.81\n",
      "F: 0.07\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.60e+08 5.10e+09 2.09e+04 1.05e+03 5.20e+02]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.29\n",
      "F: 0.65\n",
      "variant: 4 (best/2/exp)\n",
      "[3.05e+06 9.24e+07 4.16e+03 4.61e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 2 (rand/1/exp)\n",
      "[8571.87  485.42  300.01  426.09  520.26]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[7545.88  200.03  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[232242.96    300.29    300.01    412.12    520.17]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[2213.09  200.    380.98  400.11  520.5 ]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.73\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[1.83e+06 7.99e+06 1.85e+03 4.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[3.72e+06 3.68e+07 8.88e+02 4.33e+02 5.21e+02]\n",
      "\n",
      "pop_size: 172\n",
      "CR: 0.94\n",
      "F: 0.03\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.94e+06 1.22e+08 4.01e+03 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.82\n",
      "F: 0.41\n",
      "variant: 7 (rand/1/bin)\n",
      "[8624.2   501.87  300.02  408.85  520.5 ]\n",
      "\n",
      "pop_size: 176\n",
      "CR: 0.80\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[315313.54 183805.19    395.75    418.21    520.36]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.13e+07 3.74e+08 1.65e+04 5.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.87\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1804.95  200.    300.    417.39  520.31]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[684.25 200.01 300.   434.78 520.31]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[6.63e+05 3.70e+06 4.07e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[57975.43  2920.68  1293.34   444.28   520.16]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.67e+06 2.15e+08 3.59e+03 4.80e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[1.97e+08 9.91e+09 4.64e+04 2.46e+03 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.33\n",
      "F: 0.41\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2.34e+06 2.64e+07 2.42e+03 4.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.89\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[3.50e+06 1.50e+08 2.10e+03 4.81e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.31\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[9.63e+05 6.63e+03 1.88e+03 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.79\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[30505.     200.     300.     409.78   520.21]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.09\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[6.83e+06 1.85e+08 4.49e+03 5.08e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.48\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.36e+07 9.39e+08 5.07e+03 5.68e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.04\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.17e+06 2.09e+07 2.32e+03 4.31e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[2176.75 3374.76 1094.01  435.13  520.5 ]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.57\n",
      "variant: 2 (rand/1/exp)\n",
      "[3.04e+06 8.24e+07 5.10e+03 4.52e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.01\n",
      "variant: 4 (best/2/exp)\n",
      "[2.85e+08 1.26e+10 4.75e+07 3.21e+03 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[38427.32   200.21   300.     417.42   520.12]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.88\n",
      "F: 0.44\n",
      "variant: 4 (best/2/exp)\n",
      "[496.97 200.02 300.   417.39 520.33]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.81\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[1014.02  200.    300.01  426.11  520.53]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.82\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.31e+06 1.72e+07 3.88e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.81\n",
      "F: 0.05\n",
      "variant: 4 (best/2/exp)\n",
      "[3.31e+07 1.05e+09 2.04e+04 5.87e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 2 (rand/1/exp)\n",
      "[9.74e+05 1.22e+07 8.77e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 169\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.32e+06 1.58e+07 1.11e+03 4.61e+02 5.21e+02]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 571.61 3592.97  310.68  426.13  520.54]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[15790.32   200.01   300.     410.87   520.51]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[1417.43  200.02  300.    409.78  520.23]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[704.9  200.   300.93 427.17 520.01]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[9.49e+06 1.53e+08 1.51e+04 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[7.18e+05 1.71e+04 3.35e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.22e+06 9.93e+05 1.02e+03 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2.61e+06 1.90e+07 2.28e+03 4.64e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.79\n",
      "F: 0.68\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.68e+06 5.56e+08 2.53e+03 4.47e+02 5.21e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.43e+07 2.43e+09 1.39e+04 9.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[40262.73   200.     300.     417.43   520.18]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.02\n",
      "variant: 7 (rand/1/bin)\n",
      "[2.08e+06 1.18e+08 3.67e+03 4.56e+02 5.19e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.04\n",
      "variant: 4 (best/2/exp)\n",
      "[2.15e+07 4.97e+08 1.84e+04 4.72e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.29\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.23e+05 2.09e+02 3.00e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.64\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[64533.44  2971.45   321.17   428.27   520.18]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 10 (rand/2/bin)\n",
      "[198531.2  108059.49    304.39    411.72    520.53]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[27721.93   200.13   300.     408.74   520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[10443.03   200.     317.5    410.87   520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5198.36  200.    300.    417.4   520.21]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.31\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[8.10e+05 6.18e+03 2.07e+03 4.20e+02 5.19e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[8.01e+05 1.99e+07 2.00e+03 4.52e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[109.29 200.31 300.   409.78 520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.82\n",
      "F: 0.01\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.91e+08 1.08e+10 1.57e+04 3.86e+03 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.71\n",
      "F: 0.77\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.10e+06 2.79e+07 2.99e+03 4.47e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[1.95e+06 1.22e+08 2.54e+03 4.41e+02 5.18e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[31361.56   200.02   300.     418.57   520.48]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.03\n",
      "variant: 9 (best/2/bin)\n",
      "[3.56e+07 2.46e+09 2.23e+04 8.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 7 (rand/1/bin)\n",
      "[1.44e+08 1.14e+10 9.34e+04 1.27e+03 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.94e+06 8.35e+06 1.49e+03 4.28e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.80\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5511.1   210.29  300.    426.09  520.29]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[5.53e+06 7.10e+05 1.02e+03 4.65e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 10 (rand/2/bin)\n",
      "[69415.58   412.68   300.01   400.13   520.49]\n",
      "\n",
      "pop_size: 154\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[3.02e+06 8.89e+07 1.92e+03 4.51e+02 5.21e+02]\n",
      "\n",
      "pop_size: 31\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.07e+07 6.98e+08 7.74e+03 5.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.28\n",
      "F: 0.06\n",
      "variant: 4 (best/2/exp)\n",
      "[6.94e+05 2.99e+05 5.62e+03 4.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.85\n",
      "F: 0.66\n",
      "variant: 9 (best/2/bin)\n",
      "[1816.17  200.    300.    418.47  520.55]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1049.08  200.01  300.    417.39  520.33]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[10506.26   200.6    300.     418.48   520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.18   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.44\n",
      "variant: 4 (best/2/exp)\n",
      "[728.61 200.03 300.   427.17 520.3 ]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.58\n",
      "variant: 9 (best/2/bin)\n",
      "[1.55e+06 2.77e+07 1.40e+03 4.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.01\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.67e+07 3.79e+09 3.01e+04 8.08e+02 5.20e+02]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.72\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.02e+06 1.68e+09 1.11e+04 5.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 65\n",
      "CR: 0.89\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2874.76 5443.42  300.28  427.23  520.33]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[104.31 200.44 300.   426.09 520.55]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.81\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[46842.1    200.15  1169.54   427.22   520.17]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.68\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.37e+06 1.07e+07 1.63e+03 4.44e+02 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 4 (best/2/exp)\n",
      "[1.31e+07 6.18e+08 1.23e+04 4.83e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 7 (rand/1/bin)\n",
      "[18480.64   213.71   300.     408.98   520.51]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[618.89 200.   300.   427.17 520.45]\n",
      "\n",
      "pop_size: 167\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[2.44e+06 2.99e+07 3.20e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[36970.2    200.1    300.     434.78   520.16]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[3.78e+05 1.84e+06 3.99e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.79\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[211739.51   3868.56   1375.98    448.      520.19]\n",
      "\n",
      "pop_size: 160\n",
      "CR: 0.83\n",
      "F: 0.04\n",
      "variant: 4 (best/2/exp)\n",
      "[1.87e+07 3.63e+08 1.34e+04 4.83e+02 5.20e+02]\n",
      "\n",
      "pop_size: 24\n",
      "CR: 0.84\n",
      "F: 0.70\n",
      "variant: 10 (rand/2/bin)\n",
      "[8.36e+05 1.37e+07 5.55e+02 4.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[151579.89    205.76    300.      402.56    520.08]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.25\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.07e+06 1.39e+04 1.87e+03 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.95\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[32375.33  2318.63   315.22   417.59   520.24]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[114838.28 314543.25   1476.89    436.89    520.15]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[711.64 201.26 300.   427.17 520.55]\n",
      "\n",
      "pop_size: 166\n",
      "CR: 0.81\n",
      "F: 0.04\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.66e+06 1.47e+07 1.65e+03 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[1430.92  200.01  300.    426.09  520.32]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.83\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[1.18e+06 4.01e+06 7.21e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[2.31e+07 2.95e+09 2.50e+04 5.94e+02 5.20e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.29e+07 2.64e+09 1.33e+04 8.73e+02 5.20e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.35\n",
      "variant: 7 (rand/1/bin)\n",
      "[5.87e+05 4.17e+06 5.94e+02 4.29e+02 5.20e+02]\n",
      "\n",
      "pop_size: 34\n",
      "CR: 0.91\n",
      "F: 0.07\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.15e+07 8.09e+08 5.49e+03 1.08e+03 5.19e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.93\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[992.56 200.4  300.   409.79 520.36]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.21   517.75]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2769.76  200.    300.    417.39  520.34]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.79\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[14792.3    200.     300.     410.86   520.18]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.47\n",
      "variant: 4 (best/2/exp)\n",
      "[257429.23   5473.67    300.84    409.99    520.28]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.76\n",
      "variant: 9 (best/2/bin)\n",
      "[10278.09   200.03   300.19   427.17   520.44]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[65958.69   238.94   300.03   426.14   520.55]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[23062.9    200.     300.     403.25   517.34]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5.56e+05 2.58e+06 3.78e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.79\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3.21e+05 2.12e+06 5.08e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.23\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.83e+05 3.92e+03 4.71e+03 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.96\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[100.01 200.06 300.   427.17 520.45]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.83\n",
      "F: 0.38\n",
      "variant: 7 (rand/1/bin)\n",
      "[13723.19   608.08   300.06   427.35   520.46]\n",
      "\n",
      "pop_size: 169\n",
      "CR: 0.91\n",
      "F: 0.04\n",
      "variant: 4 (best/2/exp)\n",
      "[2.78e+07 1.35e+09 2.71e+04 6.98e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.69\n",
      "variant: 10 (rand/2/bin)\n",
      "[2271.39  208.48  300.    408.98  520.61]\n",
      "\n",
      "pop_size: 180\n",
      "CR: 0.70\n",
      "F: 0.04\n",
      "variant: 4 (best/2/exp)\n",
      "[3.48e+06 2.51e+07 5.28e+03 4.61e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.81\n",
      "F: 0.38\n",
      "variant: 2 (rand/1/exp)\n",
      "[96314.14  5434.89   300.54   409.03   520.29]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.91\n",
      "F: 0.04\n",
      "variant: 9 (best/2/bin)\n",
      "[3.48e+07 2.85e+09 1.99e+04 1.09e+03 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[273100.29   3379.61   1106.01    414.59    520.24]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[4505.71  200.    300.    418.47  520.52]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.44e+06 1.39e+07 4.52e+03 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[49790.07   203.57   303.64   420.55   520.14]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[16902.08   200.     300.     408.7    520.58]\n",
      "\n",
      "pop_size: 45\n",
      "CR: 0.83\n",
      "F: 0.47\n",
      "variant: 7 (rand/1/bin)\n",
      "[4930.63  256.49  300.01  417.45  520.5 ]\n",
      "\n",
      "pop_size: 185\n",
      "CR: 0.91\n",
      "F: 0.00\n",
      "variant: 9 (best/2/bin)\n",
      "[6.0e+07 5.2e+09 3.7e+04 1.2e+03 5.2e+02]\n",
      "\n",
      "pop_size: 173\n",
      "CR: 0.84\n",
      "F: 0.33\n",
      "variant: 4 (best/2/exp)\n",
      "[13753.37  3826.01   301.07   409.84   520.37]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.10\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.46e+07 1.22e+08 3.32e+03 4.68e+02 5.21e+02]\n",
      "\n",
      "pop_size: 170\n",
      "CR: 0.81\n",
      "F: 0.03\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.27e+07 1.17e+09 9.67e+03 7.34e+02 5.21e+02]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.83\n",
      "F: 0.03\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.66e+06 1.65e+07 1.63e+03 4.37e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.83\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[6.36e+05 2.45e+05 3.26e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.80\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6.36e+06 2.61e+08 1.20e+03 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     426.09   516.19]\n",
      "\n",
      "pop_size: 191\n",
      "CR: 0.82\n",
      "F: 0.03\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.74e+07 1.34e+09 9.34e+03 7.34e+02 5.21e+02]\n",
      "\n",
      "pop_size: 37\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.27e+06 2.96e+07 5.55e+03 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.06e+07 1.16e+09 2.65e+04 2.19e+03 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.64e+05 2.19e+04 3.24e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2625.9   200.    300.    426.09  520.33]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 790.54 2900.78  300.7   435.71  520.46]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13831.47   200.     300.     434.78   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40396.51   200.13   300.     408.79   520.14]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.67\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[40519.94   262.82   987.89   411.15   520.16]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[13596.79   200.04   300.     417.39   520.33]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1139.11  200.    300.    418.47  520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.82\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[9811.82  201.98  319.49  427.29  520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[26670.96   200.04   300.     418.47   520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1845.82  200.    306.95  418.48  520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[212772.38   1914.03    302.02    409.9     520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.97\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2.76e+06 3.07e+07 2.96e+03 4.39e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.97e+07 1.56e+09 1.47e+04 8.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.15e+06 1.33e+06 8.98e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4876.96  200.    300.    400.    520.21]\n",
      "\n",
      "pop_size: 175\n",
      "CR: 0.70\n",
      "F: 0.05\n",
      "variant: 7 (rand/1/bin)\n",
      "[8.84e+05 3.74e+06 1.03e+03 4.38e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1574.66  200.24  441.27  427.22  520.26]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[2.87e+05 5.22e+06 4.11e+02 4.19e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[68337.49  5160.07  1690.38   411.12   520.19]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[565.78 460.06 300.01 417.42 520.31]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.94\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6629.01  221.73  300.    426.09  520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[540.58 200.   300.   418.47 520.32]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[914.24 211.63 320.11 427.2  520.59]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[20096.91   200.     300.     409.78   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2054.66  200.    300.    426.09  520.38]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.96\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.01 200.01 300.   426.09 520.48]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.83\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[247.28 200.   300.   434.78 520.53]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.91\n",
      "F: 0.61\n",
      "variant: 4 (best/2/exp)\n",
      "[741.5  200.   300.   426.09 520.25]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+05 1.34e+07 5.85e+03 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.95\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.05e+05 4.58e+06 3.57e+02 4.06e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[3.59e+06 1.11e+07 2.70e+03 4.73e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[257.54 295.2  300.   426.09 520.31]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.65\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[53480.74  3069.31   451.86   410.67   519.44]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.31\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[21921.8    200.     300.     434.78   519.63]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1129.81  216.52  300.    418.1   515.35]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.03e+06 9.09e+05 7.99e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.30e+06 1.99e+08 6.08e+03 4.46e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.94\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1319.41  201.85  300.    418.6   520.31]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.64\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[2033.74  200.    300.01  417.42  516.74]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3669.48  200.52  300.    427.17  520.35]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.65e+06 6.11e+06 2.88e+03 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[463.63 982.83 300.   411.83 520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[1366.91  200.    300.    419.56  520.54]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.64\n",
      "variant: 5 (rand/2/exp)\n",
      "[244545.99    332.67    300.13    408.43    520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4742.18  200.    300.    411.    520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.96\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[102.01 200.03 300.   418.47 520.53]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4087.19  200.    300.    409.87  520.42]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[2561.26 1201.39  372.39  426.91  520.45]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1115.49  200.    300.01  418.59  520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1229.43 2988.47  300.28  412.27  520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[116.57 200.41 300.   418.47 520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[70223.16  3742.49  1724.86   418.94   520.22]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[97220.14   934.08   300.36   418.56   520.24]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.33e+05 2.34e+06 4.65e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2.82e+06 9.50e+04 4.37e+02 4.39e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.45e+05 1.76e+04 3.19e+02 4.07e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[8.18e+06 1.83e+08 7.63e+03 5.41e+02 5.19e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1290.35  200.    366.2   408.88  520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   418.47   517.66]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[9.32e+04 2.13e+06 3.22e+02 4.34e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[17868.26   200.01   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.66\n",
      "F: 0.66\n",
      "variant: 4 (best/2/exp)\n",
      "[86732.94   205.74   300.     411.3    520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.31\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[315368.6    3042.21   1455.64    406.73    520.11]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.69\n",
      "F: 0.51\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4536.32  204.53  300.    427.43  520.51]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.38\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.88e+06 8.88e+06 4.34e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.96\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[1.00e+05 7.95e+06 3.36e+02 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.64\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[55747.05   201.88   300.     411.11   519.89]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1265.43 2493.57 2709.72  418.66  520.47]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.64\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[809.22 200.   300.05 408.93 520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.88\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[6497.36  200.73  516.65  409.94  520.6 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.24\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[361184.13   1575.76    958.82    404.01    520.07]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1443.17  992.13  300.01  427.19  520.41]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[14420.8    270.26  1133.1    420.04   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[8249.57  200.    332.91  417.59  520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10655.55   200.     300.     400.12   520.11]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.64\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[92912.6    205.81   300.01   410.91   520.36]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10074.1    200.01   300.     426.1    520.18]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[76412.67  1519.04   301.55   410.94   520.17]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.33e+07 2.74e+09 2.15e+04 8.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[1184.7   200.    300.04  417.4   520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1298.51  200.    300.23  403.28  520.21]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[15587.82  1402.88   304.17   402.34   520.46]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[28073.52   200.     300.     419.56   520.14]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.68\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[54234.38   383.84   300.31   417.49   520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[7.60e+06 1.91e+08 1.95e+04 4.76e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.29\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.00e+05 2.87e+03 8.53e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.26\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[7.48e+05 1.77e+03 1.44e+03 4.05e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.96\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 63938.2  141279.04    305.55    430.21    520.53]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1084.46 2854.37  300.06  417.43  520.31]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[9983.75  200.    300.    417.39  520.4 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[9809.6   214.69  384.75  434.78  520.18]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.64\n",
      "variant: 9 (best/2/bin)\n",
      "[8.91e+04 4.63e+05 3.07e+02 4.21e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6.85e+06 1.96e+08 7.78e+03 4.90e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.65\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[289293.78   5458.42    305.21    418.51    520.48]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.32e+05 2.89e+04 3.29e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[4.94e+05 4.53e+06 4.03e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.83\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[15537.93  5080.03   672.     411.88   520.24]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.84\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3322.49  200.12  300.    409.78  520.24]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[60173.25   200.12   300.     401.17   520.13]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.89\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[849.32 200.06 300.   426.09 520.51]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[62751.92   205.32   300.     402.35   520.13]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[77436.69   200.53   300.     401.17   520.16]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6.31e+05 4.32e+04 3.32e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[39059.95  5291.02  1162.51   403.64   520.32]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.73\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[60453.25   200.     656.53   418.48   520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.64\n",
      "variant: 4 (best/2/exp)\n",
      "[171811.4  229479.8     311.19    410.64    520.39]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.09e+07 2.49e+08 1.41e+04 4.66e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[684.25 200.01 300.   434.78 520.31]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.66\n",
      "variant: 9 (best/2/bin)\n",
      "[572.34 200.   300.01 402.17 520.55]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.   300.   434.78 520.52]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.65\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3153.99  200.    300.    418.52  520.55]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.95\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1.07e+06 3.12e+08 3.74e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[15510.89   200.09   300.     408.77   520.46]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[281.92 313.87 300.   408.7  520.17]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.03e+07 2.66e+08 1.20e+04 5.31e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[241.73 204.99 300.   426.62 520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[8840.87  200.03  300.    426.09  520.23]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6009.63  200.    300.    426.09  510.34]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 6 (best/1/bin)\n",
      "[3.83e+06 1.78e+09 8.73e+03 4.93e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[9.24e+05 5.30e+06 4.43e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[8.56e+06 3.02e+08 4.32e+03 4.74e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[21465.07   200.     300.     409.78   520.19]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[214.59 223.91 300.   427.17 520.38]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.97\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.09e+05 7.27e+06 4.02e+02 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.64\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[1.55e+06 1.05e+06 1.19e+03 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.94\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[2.19e+07 2.37e+09 1.64e+04 6.14e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.74\n",
      "variant: 9 (best/2/bin)\n",
      "[2.39e+06 1.13e+07 1.79e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.36\n",
      "variant: 4 (best/2/exp)\n",
      "[19744.77  2482.91   762.45   418.02   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[74238.61   204.43   300.     427.17   520.18]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[7545.88  200.02  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[201478.72    227.26    300.      408.9     520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[803.76 200.   385.27 417.52 520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.3  200.01 300.   418.47 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.38e+06 4.55e+08 1.69e+03 4.78e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.21   517.75]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[122133.06    863.08    300.94    417.54    520.23]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[67672.94   205.65   943.6    417.19   520.19]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[165.71 202.99 300.   417.39 520.46]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.66\n",
      "variant: 9 (best/2/bin)\n",
      "[1.37e+06 8.45e+05 8.92e+02 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[375704.55 300715.13    719.44    450.17    520.19]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3549.25 1911.24  794.51  410.91  520.23]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[141853.57   1233.62    300.28    427.34    519.67]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.83\n",
      "F: 0.62\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2.85e+07 2.11e+09 8.83e+03 6.87e+02 5.20e+02]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.43\n",
      "variant: 9 (best/2/bin)\n",
      "[125.25 200.   300.   426.09 520.53]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[40149.28   200.04   300.     400.03   520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4967.51  200.01  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[176.1  201.81 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[18923.12   285.34   318.5    411.95   520.13]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[140394.98    707.1     300.56    400.17    520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.96\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.81e+04 4.45e+05 1.85e+03 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[72225.85   202.52   300.     402.4    520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1.50e+07 3.09e+09 1.95e+04 6.96e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4.07e+05 1.80e+04 3.19e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.64\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[6.36e+05 5.35e+04 4.32e+02 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.95\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.11e+07 2.75e+09 2.45e+04 7.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[8.31e+05 1.81e+06 4.61e+02 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[8.97e+06 7.67e+08 1.91e+04 4.84e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[612.3  228.04 300.   419.56 520.3 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.80\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[13424.68   200.     301.72   418.47   520.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.64e+06 3.24e+08 1.06e+04 5.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.92\n",
      "F: 0.51\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1975.46  425.74  300.    426.56  520.28]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[2.23e+06 1.31e+06 8.24e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.74e+06 2.28e+08 5.03e+03 4.62e+02 5.16e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[104.21 200.17 300.   434.78 520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1281.72  200.    300.    426.09  520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[142.25 207.08 300.   427.17 520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2429.18  200.02  300.    426.1   520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.97\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[39117.87   313.29   300.     400.01   520.4 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.64\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[2.11e+05 2.00e+02 3.00e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.86\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1387.6   200.    300.    427.02  520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 790.54 2900.78  300.7   435.71  520.46]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[711.64 201.26 300.   427.17 520.55]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[149286.45    924.6     301.09    426.12    520.23]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.91\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[39179.72   236.15   316.95   403.46   520.29]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.88\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[3567.4   201.53  300.    409.78  520.2 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3019.61  200.    300.    418.47  520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1474.27  200.    305.23  425.01  520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[152757.01    202.51    300.      419.61    518.56]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10074.1    200.01   300.     426.1    520.18]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[12788.4    200.01   300.     417.41   520.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[5798.88  210.09  302.19  426.1   520.25]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.83\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1832.17  201.06  300.    426.09  519.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[18452.07   200.06   300.     408.7    520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[44263.32   200.     300.41   427.24   520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8638.94  200.    300.    400.    520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[65264.18   200.01  1286.83   407.99   520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4818.07  340.78  300.    417.39  520.26]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3.30e+07 1.42e+09 1.00e+04 5.71e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.63\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.17e+05 2.14e+02 3.00e+02 4.26e+02 5.19e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1672.71  200.09  300.    418.5   520.36]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2625.9   200.    300.    426.09  520.33]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[1.73e+05 3.54e+06 3.56e+02 4.11e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[40262.73   200.     300.     417.43   520.18]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[760.16 209.64 300.   411.02 520.6 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[100.01 200.01 300.   411.95 520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[151579.89    205.76    300.      402.56    520.08]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[748.27 200.   305.3  426.09 520.46]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[407.16 472.65 300.   410.87 520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[838.3  200.27 300.   427.26 520.54]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2157.37 1741.    300.    417.71  520.56]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 435.93 1633.45  305.65  418.81  515.34]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[663.61 366.19 300.   409.78 520.3 ]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[8.98e+05 6.72e+04 3.41e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.45e+07 1.35e+09 1.28e+04 6.77e+02 5.21e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.01e+06 6.83e+05 3.39e+03 4.55e+02 5.21e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[13271.25   200.01   300.     419.56   520.07]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[46283.56   200.     860.79   427.17   520.16]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19104.05   510.86  1556.24   410.07   520.21]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.54e+05 1.41e+04 3.23e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[275.99 203.87 300.   417.39 520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[1975.51  200.    300.    417.43  520.53]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.03 200.13 300.   434.78 520.44]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[16555.24   200.     300.     418.48   520.2 ]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[9.94e+05 1.09e+07 1.49e+03 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[1107.39 1498.85  300.07  417.41  520.33]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[10808.02  3225.08   806.67   435.1    520.55]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[135889.35    201.38    300.      427.17    520.17]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[226.4  200.01 300.   426.09 520.35]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[72452.11  1378.15   300.18   417.57   520.49]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3.32e+05 8.37e+06 4.59e+02 4.56e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[2009.22 2960.3   300.11  418.7   520.47]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.96e+06 1.13e+07 1.42e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.97\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[155.58 200.05 300.   426.09 520.45]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[42405.2    201.45   300.     427.17   520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[41794.63   200.     300.     418.48   520.18]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[207.21 201.47 300.   427.17 520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3741.11  200.29  300.    434.78  520.32]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1228.06  200.05  300.    417.39  520.32]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[181960.09    202.01    300.      418.83    520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4876.96  200.    300.    400.    520.21]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.84\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[5.86e+05 2.05e+06 4.01e+02 4.20e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[66252.06  1075.97  2616.17   437.3    520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.64\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[2033.74  200.    300.01  417.42  516.74]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101.94 200.05 300.   426.09 520.46]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.97\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[16389.19  1521.8    483.8    417.62   520.45]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.56\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[290715.16   2736.25    309.05    408.9     520.46]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.92\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[5.59e+05 2.72e+06 4.38e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[9.29e+06 7.97e+08 1.16e+04 5.97e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.21e+06 3.56e+06 1.08e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1330.26  847.51  300.    418.68  520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25003.51   200.     300.     426.09   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.61\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[130871.04    207.09    300.01    417.44    520.41]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[119580.58   2258.3     304.23    426.27    520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.93\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[19142.53  1019.46   333.55   426.2    520.25]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.68\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[5439.75  200.    300.    418.56  520.5 ]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4789.22  200.    300.    434.78  520.42]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1889.08  208.19  301.75  426.2   520.59]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3920.72  200.38  300.    434.78  520.27]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[565.78 460.06 300.01 417.42 520.3 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3292.58 1205.84  300.    417.68  520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.   300.   434.78 520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[56476.75   200.01   300.     408.93   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[62751.92   205.32   300.     402.35   520.13]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[295176.07   5995.94    303.56    417.88    520.17]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[21188.     200.02   300.38   409.78   520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.86\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[22155.7    200.01   382.61   418.48   520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[15448.33   200.02   300.     417.39   520.18]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[832.38 200.91 300.   418.59 520.32]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[995.83 204.36 300.   409.81 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100152.84    208.43    424.4     408.88    520.21]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[30480.92   200.     301.57   408.72   520.15]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3583.55 1351.21  300.    427.19  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[14900.31   200.01   300.     408.7    520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[60300.88   741.22   321.43   434.78   520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.99e+07 3.16e+09 1.80e+04 6.71e+02 5.19e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[212254.39   2792.19    305.37    434.79    520.53]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1163.77  200.03  300.    434.78  515.29]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[625.61 546.08 300.01 427.18 520.33]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[195265.3     200.29    848.47    410.55    520.16]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[137127.19   1083.46    303.28    426.26    520.17]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1.30e+05 9.44e+06 1.16e+03 4.19e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[ 462.91  200.   3916.88  418.58  520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.62\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[11580.73   200.     320.63   427.18   520.52]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1890.9   200.    300.    419.56  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25579.     200.     301.83   418.53   520.16]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[112.2  203.76 300.   417.39 520.54]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[9.39e+05 1.20e+04 7.70e+02 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[368.27 200.03 300.   410.86 520.43]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.88e+04 1.08e+07 1.17e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1199.08  202.22  498.83  426.09  520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[68838.09   395.38   300.16   418.6    519.41]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2674.67  200.33  300.    427.17  520.37]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[ 519.45 1422.89  300.    419.75  520.54]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.93\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[1476.32 8417.35  300.23  417.49  520.59]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2.78e+06 6.87e+06 1.63e+03 5.52e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[228.   233.05 300.   409.78 520.32]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[141445.75    572.39    300.53    408.85    520.21]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[104.73 200.82 300.   418.47 520.54]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[42262.61   200.     301.58   426.09   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[102323.28    209.66    300.      417.47    518.8 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[20071.34   387.66   320.38   428.12   520.48]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[30343.54 66950.15   304.77   410.88   520.33]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.3  200.01 300.   418.47 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[53210.16   200.03   300.     409.78   520.11]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3.16e+08 7.88e+09 2.47e+04 2.37e+03 5.21e+02]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.24e+06 2.26e+06 5.96e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[127.55 202.28 300.   410.86 520.53]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[77846.32   200.03   302.41   410.99   520.12]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4986.66  200.    300.    434.78  520.47]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[8.97e+06 7.67e+08 1.91e+04 4.84e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.69\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.31e+06 9.37e+05 7.20e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5310.01  200.    300.    434.78  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[20219.08   200.     300.     409.78   520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[36434.45   205.71   300.     426.09   520.46]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4967.51  200.01  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[96434.95   201.58   300.01   434.78   520.47]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[218698.58   1650.19    300.35    417.61    520.23]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.93\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 62113.56 291888.19    317.2     419.12    520.61]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.56\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32701.78   200.31   300.01   409.86   520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6.49e+06 5.82e+08 9.42e+03 5.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2734.98  242.55  300.    408.7   520.28]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.49\n",
      "variant: 4 (best/2/exp)\n",
      "[4023.22  200.    300.    418.48  520.2 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 396.76 2097.13  308.4   411.03  520.58]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[121461.64   3358.05    598.7     409.87    520.55]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1649.96  200.    300.    418.48  520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2079.67 1108.92  439.98  418.66  520.44]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.96\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[29139.03   422.15   300.13   435.08   520.37]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[365962.51   2089.36    393.54    427.72    520.31]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[12444.16   200.     719.51   434.78   520.16]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1100.15  200.05  300.    434.78  520.32]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100.07 200.22 300.   418.47 520.46]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[102.93 200.01 300.   426.09 520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[7.18e+06 9.49e+08 1.30e+04 8.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[44641.8    202.35   300.     411.06   520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[14610.88   200.01   300.     434.78   520.57]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3655.28  200.02  300.    408.7   516.35]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[452.6  205.2  300.   418.47 520.44]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.34\n",
      "variant: 4 (best/2/exp)\n",
      "[2954.78  265.72  303.77  417.39  520.3 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.66\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[167549.78    217.5     300.      422.53    520.17]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1259.46 3084.26  300.02  418.53  520.47]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[104.21 200.17 300.   434.78 520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[232162.81    771.5     300.28    417.42    519.42]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.23e+06 6.38e+05 7.61e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+06 9.95e+08 6.75e+03 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.84\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[8.53e+05 1.11e+07 6.21e+02 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[6.43e+05 9.62e+05 1.49e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6.75e+05 3.39e+04 3.16e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4016.83  200.    300.    427.17  520.33]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.24\n",
      "variant: 9 (best/2/bin)\n",
      "[4.64e+07 4.86e+09 2.73e+04 1.96e+03 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.04 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2586.52  200.    470.1   427.17  520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.18   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.71\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[25736.3    200.01   300.     434.78   520.19]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.87\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[1.21e+06 1.25e+07 5.50e+02 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[49790.07   203.57   303.69   419.6    520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[19348.39   200.14   300.     417.4    520.44]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.61\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3.24e+05 2.84e+03 3.07e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[5.30e+06 7.78e+05 7.94e+02 4.29e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.75\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1450.26  200.    300.    417.39  520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1298.51  200.    300.23  403.28  520.21]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3224.63  200.    300.    434.78  520.41]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.01 300.   416.57 520.41]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[153.04 208.45 300.   426.09 520.35]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[848.32 200.   300.   417.4  520.44]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[67360.15  4321.2    464.29   431.18   520.57]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.97\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[100.31 202.42 300.   426.09 520.5 ]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.17e+06 1.53e+06 9.96e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[8.08e+07 4.87e+09 2.96e+04 1.10e+03 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[113.35 201.8  300.   409.78 520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4.53e+06 7.61e+06 6.96e+02 4.72e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   285.26 300.   426.09 520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4424.91  200.63  309.43  427.25  520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[82120.32  4071.85   814.24   429.16   520.49]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.70\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[99822.37   417.1    300.09   409.8    520.26]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.07 200.01 300.   434.78 520.57]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 790.54 2900.78  300.7   435.71  520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[37918.32   200.     873.51   409.96   520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[1.58e+05 1.75e+07 3.54e+02 4.34e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[14670.     200.     300.     427.17   517.47]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[17421.3    200.04   300.     434.78   520.43]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[286345.52   1890.4     304.82    426.19    520.26]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 1470.95 19507.91   467.96   427.79   520.57]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[16962.82   200.1    300.     408.7    520.47]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[10651.75   200.     300.     417.41   520.48]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[711.64 201.26 300.   427.17 520.55]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[10670.28 19976.22   300.6    419.9    520.37]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1488.33  212.85  300.    434.97  520.58]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[10649.73   200.     399.2    417.5    520.47]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3077.58  200.55  300.    409.78  520.23]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[877.4  200.   300.   410.97 520.48]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.63\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[7.99e+05 3.63e+04 3.61e+02 4.04e+02 5.19e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.89\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[3.24e+06 2.29e+08 7.14e+03 5.04e+02 5.18e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   200.45 300.   410.86 520.47]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[142.25 207.02 300.   427.17 520.53]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[132728.93    711.72    300.24    426.12    520.58]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6.17e+04 2.20e+07 1.59e+03 4.50e+02 5.16e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.72\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[30285.63   200.     339.94   427.2    520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.81\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[8990.73  200.    304.34  409.8   520.23]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.63\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[3.02e+05 2.25e+02 3.00e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[9.43e+05 1.87e+04 4.01e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.93\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.20e+07 1.11e+08 9.60e+03 4.37e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[9387.25  200.01  300.    426.09  520.57]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 799.31 1944.34  300.37  409.    520.54]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1996.14  200.    300.    417.42  520.44]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[232242.96    302.95    300.01    412.12    520.17]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.31e+08 5.90e+09 2.11e+04 1.43e+03 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[231312.72   2523.58    302.4     426.18    520.44]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1176.82 1288.5   300.75  434.78  520.59]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2054.66  200.    300.    426.09  520.38]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[31813.02   223.47   377.69   436.04   520.13]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[2.44e+05 4.57e+06 4.04e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.65e+06 1.01e+06 7.62e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[3.25e+07 2.82e+09 7.35e+03 5.76e+02 5.21e+02]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[11657.87   226.4    300.71   424.84   520.46]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 87229.18 227275.69   3704.29    431.59    520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[214.59 223.91 300.   427.17 520.38]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1.19e+05 3.60e+06 1.20e+03 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.71\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2249.73  200.    300.    426.09  520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2797.98  201.36  300.    437.54  520.54]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[780.8  200.03 300.   426.09 519.96]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[82624.43   502.2    300.46   418.52   520.4 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.92\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[2.20e+07 1.79e+09 2.06e+04 5.44e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.90\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[7128.28  200.    300.    434.78  520.31]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[4843.52  200.    303.31  426.16  520.44]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1819.38 1925.32  300.    438.49  520.57]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    666.96    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[122.68 201.88 300.   410.86 520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.44\n",
      "variant: 4 (best/2/exp)\n",
      "[29601.74   201.63   738.81   426.3    520.11]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.93\n",
      "F: 0.66\n",
      "variant: 9 (best/2/bin)\n",
      "[1.57e+05 1.17e+06 3.31e+02 4.12e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1.82e+06 7.46e+07 6.59e+03 4.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[3.04e+05 1.06e+07 3.95e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4226.43  200.    300.    427.17  520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.88\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[402.39 200.02 300.   417.39 520.26]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.62\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[98691.14   201.26   438.42   426.09   520.11]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   285.26 300.   426.09 520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[75542.93   553.13   309.25   418.91   520.26]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.85\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[44342.55   200.     333.76   402.17   520.24]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[45704.06   200.41   300.     408.74   520.15]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.00e+06 5.66e+08 2.33e+04 6.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[279827.63   1956.73    303.86    426.21    520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10534.63   200.     300.     426.09   520.17]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.71\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1379.56  200.    300.    427.32  520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[55798.69  3391.93   436.01   431.18   520.24]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.04 200.61 300.   417.39 520.52]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[38547.68   200.     300.     418.48   520.16]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1129.81  216.52  300.    418.1   515.35]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1447.6   200.    301.21  418.53  520.53]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1567.89  200.    300.    418.47  520.33]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1606.39  200.25  300.    418.47  519.83]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[60881.17   200.21   300.     411.08   520.14]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4727.63 1085.49  300.06  418.38  520.48]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[5.24e+06 2.41e+08 1.69e+03 6.75e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[230906.27   1053.1     301.67    409.9     520.42]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2079.67 1108.92  439.98  418.66  520.44]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1649.96  200.    300.    418.48  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     426.09   516.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12765.38   200.     305.69   409.78   520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.94\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[33720.05  1453.38   309.81   410.04   520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[29972.26   200.01   300.     411.95   520.15]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[7445.14 1695.54  587.21  433.52  520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[540.58 200.   300.   418.47 520.32]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.05e+07 3.16e+09 1.80e+04 6.71e+02 5.19e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.49e+05 1.89e+04 3.20e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1330.26  847.51  300.    418.68  520.46]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.72\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[6.37e+05 2.77e+06 1.59e+03 4.35e+02 5.16e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3292.58 1205.84  300.    417.68  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6.85e+06 1.96e+08 7.78e+03 4.90e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.40e+06 6.10e+05 8.36e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25003.51   200.     300.     426.09   520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[1707.45  200.86  301.26  435.62  520.57]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[27449.02   200.     300.     426.09   520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[15487.97   200.     302.3    426.22   520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[89003.7    202.69   300.     410.65   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.14e+07 1.79e+09 7.93e+03 6.97e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[ 819.61 1099.64  300.16  411.21  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[8840.87  200.03  300.    426.09  520.23]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.93e+06 9.02e+05 7.98e+02 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.27e+07 1.82e+09 1.46e+04 6.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.21   517.75]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[100165.94    200.04    300.      417.52    520.53]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[6.64e+06 2.59e+03 4.90e+03 4.37e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[241.73 204.99 300.   426.62 520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[50918.32  1851.34   310.     417.89   520.51]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[2176.75 3374.76 1094.01  435.13  520.5 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[8.19e+05 2.03e+07 2.45e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[106313.47    667.98    300.58    426.11    520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.48e+05 3.91e+03 3.06e+02 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.60e+06 3.00e+08 4.56e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[803.76 200.   385.27 417.52 520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5750.66  200.    301.92  426.09  520.46]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[7.84e+05 6.13e+08 7.78e+03 4.66e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[2.35e+05 5.30e+06 3.61e+02 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.64\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[16060.08   200.04   300.     426.11   520.58]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.94\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[64051.12  1203.88   302.12   427.66   520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3769.03  200.    300.    434.78  520.56]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[109189.13    200.63    302.78    410.72    520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3.25e+05 3.68e+06 3.73e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1464.86  200.    300.01  434.78  520.54]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[138127.2     219.11    300.02    409.87    520.16]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[3161.45  200.    302.57  408.79  520.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[23944.     200.04   300.     402.17   520.17]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[125.75 298.98 300.   409.78 520.36]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25579.     200.     301.83   418.53   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[12444.16   200.     719.51   434.78   520.16]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.72\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.18e+05 1.53e+04 3.17e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[7.99e+06 1.20e+09 1.53e+04 7.36e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[230.17 200.   300.   434.78 520.35]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.95\n",
      "F: 0.52\n",
      "variant: 5 (rand/2/exp)\n",
      "[104887.53   1588.25    558.13    401.11    520.36]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3143.2   202.12  300.    417.6   520.43]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+06 9.95e+08 6.75e+03 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[7.09e+05 5.50e+06 5.97e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   285.26 300.   426.09 520.54]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4.26e+07 2.02e+09 1.92e+04 9.39e+02 5.21e+02]\n",
      "\n",
      "pop_size: 72\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[4.58e+05 5.17e+03 3.05e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[995.83 204.36 300.   409.81 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[18072.58   200.09   300.02   417.39   520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.06e+07 1.33e+09 1.27e+04 7.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.73e+06 3.11e+08 1.73e+03 4.75e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.99\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[306270.51  16688.84   2603.01    439.51    520.46]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[10092.91  1237.19   331.7    435.77   520.5 ]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1828.2   200.    300.    427.17  519.88]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[6397.14  200.    300.    408.75  520.42]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[174.61 221.05 300.   427.17 520.09]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1133.22  200.    300.    426.09  520.34]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[161798.08    535.36    300.11    426.1     520.26]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[3213.19  200.02  300.    410.86  520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4414.33  200.    300.    419.56  520.15]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[72010.68   200.36   300.     418.61   520.16]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6247.23 2962.57  307.44  425.14  520.51]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[707.26 204.07 300.   434.82 515.38]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[118855.43   3793.06    307.21    400.3     520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[116105.9     266.51    300.03    434.78    520.27]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[101.28 200.16 300.   401.08 520.36]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1.56e+05 8.14e+07 6.43e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4339.98  200.    306.17  410.15  520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4.13e+06 1.15e+07 8.89e+02 4.39e+02 5.18e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.71\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2198.86  200.    300.    426.09  520.51]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[4.28e+06 5.45e+07 2.99e+03 4.59e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.89e+06 1.59e+07 3.15e+03 4.53e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.42e+07 8.69e+09 2.54e+04 1.27e+03 5.21e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[5.28e+05 1.74e+04 3.20e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[ 645.72 1002.81  300.    434.79  520.53]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[201478.72    227.26    300.      408.9     520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[101.79 200.34 300.   409.78 520.4 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[281.92 313.87 300.   408.7  520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.30e+06 1.99e+08 6.08e+03 4.46e+02 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8.67e+05 2.84e+04 4.41e+02 4.07e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.08e+06 7.04e+02 7.69e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6.24e+05 3.38e+04 3.27e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[135889.35    201.38    300.      427.17    520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.62\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[11580.73   200.     320.63   427.18   520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[419.11 207.3  320.61 417.57 520.55]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2140.02  200.    300.    426.09  520.5 ]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[554.28 816.32 300.02 418.48 520.37]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.20e+07 2.06e+09 1.52e+04 4.99e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[13434.79   200.     316.71   411.01   520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[9.32e+04 2.13e+06 3.22e+02 4.34e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.30\n",
      "variant: 9 (best/2/bin)\n",
      "[4.08e+06 7.71e+07 4.67e+03 4.23e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[181443.04  18779.93    520.45    446.12    520.54]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.62\n",
      "variant: 4 (best/2/exp)\n",
      "[45073.85   200.5    300.     409.85   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1129.81  216.52  300.    418.1   515.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.84\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[9.56e+05 8.93e+05 4.40e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.70e+07 3.72e+09 1.97e+04 5.66e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1281.72  200.    300.    426.09  520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.03e+07 1.21e+09 9.13e+03 5.59e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[795.87 200.   371.85 419.56 520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[47236.15   200.     300.     426.09   520.39]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.84\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.46e+06 4.40e+06 4.64e+02 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[134117.2     200.09    300.      408.71    520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[102323.28    209.66    300.      417.47    518.8 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.92\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[151.44 404.47 300.   426.09 520.34]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.88e+06 7.08e+08 1.44e+04 7.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2586.52  200.    470.1   426.1   520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[15929.21   200.     874.76   402.27   516.97]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.44e+05 8.81e+05 3.95e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.47e+07 5.98e+09 2.63e+04 8.47e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[5.51e+07 4.35e+09 1.75e+04 8.61e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6.32e+05 2.65e+04 3.26e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.66\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[167549.78    217.5     300.      422.53    520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.93\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 62113.56 291888.19    317.2     419.12    520.61]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[38411.79   200.16   300.     417.52   520.12]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.89\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[2177.7  2889.19  300.01  422.37  520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    663.81    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[23866.54  4405.08   605.37   419.61   520.31]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.59\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[51285.95   201.48   300.03   426.12   520.44]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1.44e+06 5.37e+08 4.28e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[4159.48  200.03  300.    434.78  515.43]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[21465.07   200.     300.     409.78   520.19]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.21e+05 4.51e+05 5.59e+02 4.44e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[124.95 202.29 300.   417.39 520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[704.9  200.   300.93 427.17 520.01]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.72e+06 5.98e+07 2.96e+03 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[478.67 200.   300.   410.86 520.36]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.24e+05 5.42e+05 8.60e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[102.93 200.01 300.   426.09 520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.18   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[25181.4    200.     304.4    417.39   520.17]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.72\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2922.43  200.    300.    427.17  520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.86\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[39066.76   200.04   300.     403.25   520.23]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.61\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[8.47e+05 2.91e+04 4.69e+02 4.07e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.13e+07 1.10e+09 1.64e+04 4.93e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[848.32 200.   300.   417.4  520.44]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1996.14  200.    300.    417.42  520.44]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100152.84    207.53    424.4     408.88    520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.04e+06 7.21e+06 4.52e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[355.91 206.84 300.   409.78 520.55]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[198123.49   7619.2     304.74    434.79    520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[40814.17   200.     310.97   426.09   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[65653.41   200.16   300.     410.7    520.17]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.92\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[3128.88  200.01  300.    418.49  520.29]\n",
      "\n",
      "pop_size: 45\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[9.47e+05 4.53e+05 3.63e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[35897.96   331.61   341.71   409.88   520.48]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.69\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[94052.77  1374.09   300.73   426.11   520.23]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.56\n",
      "variant: 9 (best/2/bin)\n",
      "[142133.06   6595.11    308.61    410.18    520.5 ]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.69\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[33249.02   200.02   371.02   416.66   518.06]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.95e+05 3.97e+03 3.10e+02 4.18e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.43e+07 3.76e+09 1.40e+04 8.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.94e+06 1.76e+06 1.49e+03 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3.91e+06 3.29e+07 5.13e+03 4.60e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.84\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[7.59e+05 1.12e+06 4.41e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1.58e+07 4.97e+09 1.56e+04 5.77e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[6702.59  200.    300.    434.78  520.13]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.92\n",
      "F: 0.37\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.98e+06 2.62e+07 1.86e+03 4.55e+02 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.89\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1098.55  260.88  300.    434.78  520.25]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[8.20e+04 8.78e+06 1.59e+03 4.22e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13831.47   200.     300.     434.78   520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.07e+06 2.68e+08 9.06e+02 4.57e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.65\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.06e+06 5.93e+05 1.15e+03 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[236.69 250.22 300.   427.17 520.37]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1176.82 1288.5   300.75  434.78  520.59]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[176.1  201.81 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[53210.16   200.03   300.     409.78   520.11]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1229.43 2988.47  300.28  412.27  520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3.09e+05 2.32e+02 3.00e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[838.3  200.27 300.   427.26 520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 5 (rand/2/exp)\n",
      "[49051.19   200.2    315.68   415.1    520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.71\n",
      "variant: 6 (best/1/bin)\n",
      "[47443.18   219.77   300.06   434.78   520.32]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.05 200.23 300.   419.56 520.45]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[102081.92   1326.43    325.88    426.13    520.11]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4112.57  215.32  300.56  418.49  520.31]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[750.06 202.43 300.   426.09 520.3 ]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[1.17e+06 1.96e+06 5.14e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.55e+07 4.24e+09 2.68e+04 1.09e+03 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[7094.13  200.    300.    409.78  520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[3.06e+05 2.09e+02 3.00e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 18\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[190873.83    724.65    302.32    409.98    520.18]\n",
      "\n",
      "pop_size: 69\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[384.5  210.03 300.   418.89 520.54]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[2.47e+06 1.66e+08 6.39e+03 4.55e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[429.27 386.11 300.01 408.7  520.32]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1163.77  200.03  300.    434.78  515.29]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[5169.08 4192.63 1079.69  417.53  520.41]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[1.58e+06 1.18e+06 1.45e+03 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.94\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.31e+07 1.08e+09 1.38e+04 4.89e+02 5.20e+02]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[101640.81   4104.15    301.2     426.17    520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[851.55 200.   300.   426.09 520.26]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[37918.32   200.     873.51   409.96   520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   200.02 300.   426.09 520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3741.11  200.03  300.    434.78  520.32]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[5.93e+05 6.95e+05 2.10e+03 4.35e+02 5.18e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.92\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[84257.67   847.21   852.22   442.7    520.3 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[189837.68   2080.08    301.61    417.56    520.54]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[623.47 200.02 300.   408.7  520.34]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3655.28  200.02  300.    408.7   516.35]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[7.90e+05 6.20e+06 5.35e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.21   517.75]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.84\n",
      "F: 0.54\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.72e+07 1.02e+09 9.31e+03 6.17e+02 5.17e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+07 3.85e+06 1.26e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[83672.67  1914.15   300.3    417.47   520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[9.22e+06 2.31e+09 1.54e+04 6.79e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2.60e+05 4.88e+05 1.33e+03 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.89\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[7228.48  209.42  300.12  409.92  520.28]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.43\n",
      "variant: 9 (best/2/bin)\n",
      "[6876.92  200.06  300.    427.17  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[49790.07   203.61   303.64   419.6    520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[15448.33   200.02   300.     417.39   520.18]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[16714.87  2279.98   300.     434.79   520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[15929.21   200.     874.76   402.27   516.97]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.01 300.   418.47 520.41]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3551.29 1351.21  300.    427.19  520.54]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3344.93  200.    300.    417.39  520.51]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[159.41 200.   300.   409.78 520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[80662.73   202.08   844.81   420.26   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[212.13 200.   300.   434.78 520.54]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[86317.02   901.4    429.12   433.58   520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[30859.98   200.17   300.37   427.17   520.21]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[ 100.18 1363.79  300.    427.17  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6.82e+06 5.82e+08 9.42e+03 5.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.49e+05 1.85e+04 3.12e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[30644.79   200.8    300.     418.48   520.45]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1702.38  200.04  300.    418.47  520.29]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.98e+06 7.21e+05 1.50e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[201478.72    227.26    300.      408.9     520.17]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.62\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[236957.73    269.76    300.08    409.49    520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1637.78  200.    307.17  426.2   520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1574.66  200.25  441.27  427.22  520.26]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[9755.98  200.    300.    434.78  520.46]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[108.29 213.55 300.   427.17 520.35]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[17103.27   200.     300.     419.56   520.19]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[251.05 550.13 300.   426.09 520.31]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1717.05  200.    300.16  426.09  520.43]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[114.67 201.84 300.   418.47 520.5 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[48162.58   235.46   445.16   418.84   518.09]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[181443.04  18779.93    520.45    446.12    520.54]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[162.02 213.17 300.   417.39 520.36]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3.34e+05 3.07e+07 1.43e+03 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4414.33  200.    300.    419.56  520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[40149.28   200.04   300.     400.03   520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[ 519.45 1422.89  300.    419.75  520.54]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[131589.56   3297.12    302.04    418.57    520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.90\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1.57e+07 1.38e+09 1.77e+04 5.81e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[762.17 205.79 300.   417.39 520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[8342.06  200.    300.    426.09  520.13]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.78e+05 2.46e+04 3.63e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.04e+07 1.33e+09 1.73e+04 6.59e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.61\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1305.49  200.    300.43  417.63  520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[112595.39    212.39    300.      417.48    520.13]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.93\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[9.19e+05 5.96e+03 3.09e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    663.81    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4876.96  200.    300.    400.    520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[4.06e+05 4.48e+06 7.44e+02 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[195153.72   3458.9     304.6     426.12    520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[7544.86  200.    300.    409.82  520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[59696.83   200.     335.89   427.23   520.19]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[89003.7    202.69   300.     410.89   520.14]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[724.52 200.   300.   417.39 520.43]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1086.23  321.75  300.    409.94  520.59]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.45e+07 1.35e+09 1.22e+04 6.68e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[118771.45   4089.06    301.43    418.62    520.49]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.02 300.   401.08 520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.71\n",
      "F: 0.54\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[7.99e+05 4.37e+07 2.44e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1634.44  200.85  300.    427.26  520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 5 (rand/2/exp)\n",
      "[86228.37   208.77   300.     403.78   520.18]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.01 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[8425.48  200.05  300.    417.4   520.28]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.36e+05 5.86e+03 3.07e+02 4.35e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[64904.15   200.17   300.     408.74   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[42405.2    201.45   300.     427.17   520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.97e+03 1.58e+06 3.19e+02 4.06e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 2 (rand/1/exp)\n",
      "[1.01e+06 2.17e+03 3.91e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[2.59e+05 6.37e+05 7.39e+02 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1447.6   200.    301.21  417.44  520.53]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[13291.96   200.42   300.04   418.49   520.28]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.68\n",
      "F: 0.43\n",
      "variant: 9 (best/2/bin)\n",
      "[24834.4    201.47   300.     426.1    520.35]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.43\n",
      "variant: 9 (best/2/bin)\n",
      "[3420.43  200.07  300.    419.56  520.45]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4831.17  455.12 1440.98  426.1   520.22]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[4956.69  200.    300.    417.39  520.36]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[438.89 400.72 300.   408.7  520.5 ]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[231312.72   2523.58    302.4     426.18    520.44]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.34e+07 2.85e+09 2.19e+04 8.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1159.37 3000.24  303.16  420.32  520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.56\n",
      "variant: 9 (best/2/bin)\n",
      "[21256.26   200.     300.     417.41   520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2.77e+06 3.58e+07 2.80e+03 4.37e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101.94 200.05 300.   426.09 520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[23062.9    200.     300.     403.25   517.34]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[28073.52   200.     300.     419.56   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.79e+07 1.39e+09 1.52e+04 5.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.93\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.30e+04 4.45e+05 8.56e+02 4.19e+02 5.16e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.64\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[117369.95   1265.34    301.98    409.84    520.21]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.87e+05 2.62e+06 3.57e+02 4.35e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 2 (rand/1/exp)\n",
      "[7.89e+05 9.57e+07 1.66e+03 4.47e+02 5.19e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.68\n",
      "F: 0.46\n",
      "variant: 4 (best/2/exp)\n",
      "[3.17e+05 5.15e+03 3.10e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.76\n",
      "variant: 4 (best/2/exp)\n",
      "[9420.    202.84  300.    419.56  519.87]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1488.33  212.85  300.    434.97  520.58]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1330.26  847.51  300.    418.68  520.46]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[2312.37  204.38  300.    427.17  520.53]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[2.06e+07 2.84e+09 9.76e+03 6.30e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4362.29  200.03  352.4   412.07  520.41]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[2674.46  200.    300.    417.39  520.13]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[4159.48  200.03  300.    434.78  515.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[2.56e+07 1.66e+09 1.23e+04 4.75e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[5.21e+05 8.21e+03 3.16e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1.22e+05 9.21e+05 1.58e+03 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[105.32 200.15 300.   418.47 520.59]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1149.93  205.29  300.    422.87  520.44]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4339.98  200.    306.17  410.15  520.48]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1028.92  200.    300.    426.09  520.38]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.91\n",
      "F: 0.75\n",
      "variant: 9 (best/2/bin)\n",
      "[623.08 206.37 300.   427.17 520.63]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.70\n",
      "F: 0.36\n",
      "variant: 5 (rand/2/exp)\n",
      "[8.26e+05 2.54e+04 3.19e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.33e+06 1.22e+06 1.85e+03 4.54e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.20e+05 1.78e+06 3.86e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100152.84    208.43    424.4     408.88    520.21]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.85\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[760.06 200.   300.   417.39 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25579.     200.     301.83   418.53   520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.89\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 556.18 2395.28  309.35  411.06  520.49]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[112.12 211.96 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[9387.25  200.01  300.    426.09  520.57]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4302.57   312.57   426.09   520.43]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.89e+06 2.86e+08 2.15e+03 4.51e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.34e+07 1.10e+09 1.03e+04 7.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.62\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[212165.02   5966.66    311.26    417.58    520.39]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[2939.05  200.01  300.    410.86  520.43]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.89\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1156.02  200.    300.    419.85  520.51]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.36\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.15e+06 1.31e+05 8.81e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.57\n",
      "variant: 4 (best/2/exp)\n",
      "[43160.69  1420.16   736.33   401.28   518.69]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[5750.66  200.    301.92  426.09  520.46]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4459.9   200.    304.12  408.88  520.41]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.89\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[466.29 203.02 300.   426.22 520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.47\n",
      "variant: 9 (best/2/bin)\n",
      "[34002.55  5240.54   300.1    426.95   520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[56982.25  6660.94   544.13   426.68   520.22]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[9.13e+05 1.45e+06 4.31e+02 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1.04e+07 5.82e+08 1.02e+04 4.89e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.60e+06 1.52e+06 9.27e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.23   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.64\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1137.59  200.    320.31  426.09  520.49]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.96\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[100.01 200.06 300.   427.17 520.45]\n",
      "\n",
      "pop_size: 33\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[9.18e+05 6.48e+04 3.49e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[139312.54   1224.63    300.79    417.48    520.55]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[6.14e+06 2.66e+09 1.36e+04 6.76e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4.95e+05 3.43e+06 6.30e+02 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.65\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[294675.36   2322.75    305.23    426.16    520.44]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[10322.11   200.     300.01   426.12   520.43]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1702.38  200.04  300.    418.47  520.29]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[16757.68   802.59   335.49   410.02   520.15]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[107.32 200.02 300.   417.39 520.47]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.14e+07 1.79e+09 7.93e+03 6.97e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[37231.06   200.34   300.     418.5    520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[7198.46  200.    300.    427.17  520.37]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[4.36e+07 7.85e+09 2.50e+04 1.10e+03 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.47e+05 2.11e+04 3.12e+02 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[184591.56    431.59    300.32    426.12    520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3.34e+05 3.07e+07 1.43e+03 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.65\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[8433.42  200.    300.    434.78  520.5 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.01e+07 2.88e+09 1.75e+04 7.51e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[225340.13   9178.34    303.14    418.6     520.58]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1098.55  200.06  300.    426.36  520.33]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.68\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[5493.02  200.01  300.    418.48  520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1474.27  200.    305.23  426.09  520.48]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.06 300.   427.17 520.52]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[803.76 200.   385.27 417.52 520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[5.99e+05 2.56e+07 3.58e+03 4.51e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.66e+06 5.83e+05 6.18e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6247.23 2962.57  307.44  425.14  520.51]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.28\n",
      "variant: 9 (best/2/bin)\n",
      "[1.15e+07 1.46e+07 4.62e+03 4.39e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.83\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[788.41 204.79 300.   409.78 520.32]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[11015.78   268.18   543.08   427.17   520.22]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[5798.88  210.09  302.19  426.1   520.25]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.84\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1392.19  200.73  300.    418.6   520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[7544.86  200.    300.    409.82  520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3224.63  200.    300.    434.78  520.41]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4331.14  200.02  300.    418.47  520.35]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[5.93e+05 6.95e+05 2.10e+03 4.35e+02 5.18e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[35897.96   331.61   341.71   408.93   520.48]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[106767.09    231.4     300.06    427.17    520.43]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.89\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[602.86 200.   300.   426.09 520.22]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[147435.23   1230.72    300.89    434.79    520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.98\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+07 5.56e+08 1.44e+04 4.66e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[37863.21   200.13   339.07   418.54   520.18]\n",
      "\n",
      "pop_size: 64\n",
      "CR: 0.70\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[4511.44  200.01  300.    417.4   520.5 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[72010.68   200.36   300.     418.61   520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4414.33  200.    300.    419.56  520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[401.38 266.   300.   417.39 520.36]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[88974.54  1602.45   473.54   409.     520.5 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21414.55   200.     317.34   432.61   520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[ 209.89 1049.77  300.    426.09  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[16327.16   223.53   305.56   418.48   520.25]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.83\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2842.8   200.38  300.    427.17  520.32]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[8342.06  200.    300.    426.09  520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.49\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[2.50e+05 2.19e+02 3.00e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.46e+06 5.75e+06 3.31e+03 4.31e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101.94 200.05 300.   426.09 520.46]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[234168.87   1427.72    301.65    434.79    520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.99e+07 3.16e+09 1.80e+04 6.71e+02 5.19e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.96\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.03 300.   426.09 520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2797.98  201.36  300.    437.54  520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4876.96  200.    300.    400.    520.21]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[29972.26   200.01   300.     411.95   520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[6.75e+05 2.79e+05 4.06e+03 4.49e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.66\n",
      "variant: 9 (best/2/bin)\n",
      "[1.37e+06 8.45e+05 8.92e+02 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[3.91e+06 3.29e+07 5.13e+03 4.60e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[314.59 406.94 300.   401.09 520.32]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.87\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[2127.42  622.68  300.02  418.48  520.32]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.64\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1233.67  200.    300.05  426.19  520.46]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[77144.06   212.09   300.     401.43   520.15]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[9008.08 1126.62  422.14  417.84  520.53]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[109.29 200.31 300.   409.78 520.57]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.12e+06 7.69e+07 5.46e+03 4.75e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 219.5  4308.88  300.08  401.27  520.56]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[92203.95   203.63   300.     409.79   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[3.98e+05 2.00e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.72e+06 5.98e+07 2.96e+03 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.95e+05 3.97e+03 3.10e+02 4.18e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.24e+07 7.77e+09 1.82e+04 1.00e+03 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.94\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2912.98  200.24  300.    410.86  515.28]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[45704.06   200.41   300.     408.74   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[152757.01    202.51    300.      419.61    518.56]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.92e+06 6.85e+05 8.56e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[9.22e+06 2.39e+09 1.57e+04 6.79e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1280.71 1004.14  300.    434.78  520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3769.03  200.    300.    434.78  520.56]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101505.54    906.73    300.89    427.17    520.48]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[41613.14   200.     300.     418.47   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     425.13   516.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[1574.66  200.24  441.27  427.22  520.26]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10534.63   200.     300.     428.26   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.88\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6479.49  200.67  300.    435.33  520.58]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[45909.41   394.3    300.06   417.41   519.99]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    663.81    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[264620.41   9331.53    301.32    420.96    520.47]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[243426.9    3947.93    865.89    437.93    520.35]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 10 (rand/2/bin)\n",
      "[2727.57 8508.88  300.31  418.69  520.57]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.75\n",
      "variant: 4 (best/2/exp)\n",
      "[2.32e+05 2.30e+02 3.00e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[42965.84   200.45   313.77   418.48   520.2 ]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[497.48 355.28 300.   419.56 520.34]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[89249.43   200.2    300.     417.41   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[49830.13   202.76   300.02   422.76   517.43]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.99\n",
      "F: 0.70\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.65e+06 4.50e+08 2.10e+03 4.94e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[23866.46   257.84   300.     427.17   520.56]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[135171.95 183291.66    908.88    410.43    520.45]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.06e+05 1.74e+04 3.35e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[59022.8    356.13  2114.2    417.48   520.55]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 62\n",
      "CR: 0.92\n",
      "F: 0.55\n",
      "variant: 9 (best/2/bin)\n",
      "[509.77 483.98 300.01 434.79 520.51]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.69\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[9.64e+06 1.27e+09 1.87e+04 7.14e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[10649.73   200.     399.2    417.5    520.47]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 203.85 5292.51  300.16  417.52  520.45]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[9.99e+05 1.47e+05 3.46e+02 4.28e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.56e+06 1.71e+08 7.57e+03 4.94e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[7174.71  200.    311.88  418.63  520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.38e+06 4.55e+08 1.69e+03 4.78e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.74\n",
      "variant: 9 (best/2/bin)\n",
      "[110238.37    393.05    300.11    417.42    520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[18837.83   200.     300.     402.17   520.14]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.05 200.23 300.   419.56 520.45]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1081.34  200.    300.    410.86  520.52]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[1586.77  200.14  300.    410.87  520.31]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[54454.25   517.     300.03   434.78   520.24]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2351.73  200.    300.    409.79  520.38]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.82\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6046.09  200.    300.    408.7   520.21]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2140.02  200.    300.    426.09  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[6644.67  200.    300.    408.71  520.2 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[35833.99   202.06   300.     408.86   520.12]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.2  300.   419.56 520.41]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13129.17   200.02   300.14   401.21   517.75]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.27e+07 6.60e+08 7.76e+03 9.14e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.95\n",
      "F: 0.51\n",
      "variant: 10 (rand/2/bin)\n",
      "[9.34e+04 4.48e+06 3.70e+02 4.17e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.73\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[ 71989.52 178021.81    999.57    457.18    520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[108591.05    628.28    300.88    434.78    520.45]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[25859.46   702.55   427.92   420.09   520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6009.63  200.    300.    426.09  510.34]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.62\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[236957.73    269.76    300.08    409.49    520.17]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3920.72  200.38  300.    434.78  520.27]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[201.86 209.29 300.   417.39 520.6 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.10e+05 1.43e+07 3.19e+02 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1447.6   200.    301.21  418.53  520.53]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[ 209.89 1049.77  300.    426.09  520.54]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.59e+05 4.39e+07 2.14e+03 4.70e+02 5.21e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[6.12e+05 2.76e+04 3.34e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2.66e+07 1.43e+09 1.83e+04 8.40e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.1  300.   418.47 520.53]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[15929.21   200.     874.76   402.27   516.97]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.00e+06 7.08e+08 1.34e+04 7.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.38 300.   426.09 520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 501.28 1124.4   300.    401.09  520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   511.61 300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5198.36  200.    300.    417.4   520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[31368.3    200.01   300.     409.9    520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[56612.55   202.73   300.     411.33   520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2586.52  200.    470.1   427.17  520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1736.12  874.61  301.24  418.57  520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[23944.     200.04   300.     402.17   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.70e+05 5.01e+07 3.05e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[195478.89    763.01    301.68    434.78    520.52]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[151179.41    516.81    300.72    434.79    520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.99\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38497.99  3619.05   561.08   411.15   520.5 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[230905.44    411.31    300.08    409.92    520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.87\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[689.66 205.46 300.   409.78 520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.71\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1282.86  200.    300.    434.78  520.45]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.69\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[51357.44  2305.72   300.65   409.88   520.25]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[11693.43   200.     300.     417.39   520.25]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[895.12 200.   572.01 426.17 520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[105.97 200.28 300.   426.09 520.59]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+07 3.85e+06 1.26e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[1.97e+06 9.92e+05 7.10e+02 4.29e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[19488.91 39174.64   301.07   409.63   520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.68e+05 2.54e+06 2.47e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.90\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[9952.14  201.55  321.75  418.47  520.21]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2799.62  200.02  300.    427.17  520.49]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.98e+06 3.87e+07 3.85e+03 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[ 462.91  200.   3916.88  418.58  520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.89e+06 2.86e+08 2.15e+03 4.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[1.29e+06 1.17e+06 1.01e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[142.25 207.08 300.   427.17 520.53]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[116704.73    220.21    300.      417.87    518.19]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[33903.86   200.     300.     418.47   520.17]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.93\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4.76e+05 9.26e+06 2.76e+03 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[211657.51    935.55    300.91    426.11    520.43]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1165.06  200.    300.    426.2   520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[217391.47    322.53    300.05    401.35    520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[149810.94    208.8     300.01    418.5     520.53]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1280.71 1004.14  300.    434.78  520.52]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[11827.3  32040.17   301.13   410.07   520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.73e+05 3.00e+06 7.17e+02 4.35e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.88e+04 1.08e+07 1.17e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[6.07e+05 1.65e+05 3.69e+02 4.16e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[60881.17   200.21   300.     411.08   520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[23328.93   200.     300.04   417.41   520.21]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[4090.76  200.    492.38  417.57  520.42]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.01 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[565.78 460.06 300.01 417.42 520.31]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[136536.93    200.01    316.97    426.13    520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[2049.08  630.33  360.6   418.6   520.51]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.97e+05 1.99e+08 1.57e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10010.29   200.     306.72   400.34   520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.92\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[5557.21  206.44  300.    426.32  520.34]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.38e+06 5.45e+08 5.60e+03 1.36e+03 5.20e+02]\n",
      "\n",
      "pop_size: 66\n",
      "CR: 0.90\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[1.15e+06 3.59e+07 8.00e+02 4.38e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[251.05 550.13 300.   426.09 520.31]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[179387.25    210.79    300.      403.37    520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3572.62  208.3   300.    426.09  520.51]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 1.00\n",
      "F: 0.54\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.36e+07 5.51e+09 3.29e+04 1.60e+03 5.21e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[797.6  200.   300.   426.09 520.5 ]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3378.63  200.    300.    434.78  520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[44929.36   200.18   300.     408.7    520.15]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2711.64  215.47  300.    411.06  520.43]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.84\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[82120.27   853.21   352.28   403.33   520.24]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[1.54e+06 7.66e+05 8.24e+02 4.16e+02 5.19e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.74e+06 2.28e+08 5.03e+03 4.62e+02 5.16e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[181443.04  18779.93    520.45    446.12    520.54]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19973.18   200.     300.     417.52   520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3019.61  200.    300.    418.47  520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.92\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[5.68e+06 3.02e+07 1.01e+04 4.60e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[121746.25   1058.9     301.49    402.42    520.19]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[469.45 919.55 300.01 426.09 520.29]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[293649.51   1550.11   1555.67    453.19    520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4414.33  200.    300.    419.56  520.15]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[66743.75   200.23   300.     409.83   520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.58\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[52273.92   200.26   300.     419.56   520.4 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.93\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1980.12  200.    300.    417.39  520.27]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.97e+03 1.58e+06 3.19e+02 4.06e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.97e+05 1.98e+08 1.45e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.25e+06 2.80e+06 4.75e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.19e+07 3.41e+09 2.28e+04 7.79e+02 5.21e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.55\n",
      "variant: 9 (best/2/bin)\n",
      "[1187.62  200.    300.    409.78  520.48]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[60786.22 72221.45   359.1    431.27   520.58]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.71\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[39842.78   200.41   300.     400.02   520.15]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1485.23  200.02  300.    419.56  520.29]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[52336.98   769.15   478.45   429.48   520.56]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.99\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.84e+05 1.15e+07 3.73e+02 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[251.85 359.57 300.   417.39 520.33]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4.65e+05 3.15e+04 3.25e+02 4.29e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3549.25 1911.24  794.51  409.96  520.23]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 10 (rand/2/bin)\n",
      "[4649.25 6462.52  300.16  409.91  520.55]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.98\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[100.38 229.18 300.   426.09 520.39]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.59e+06 1.06e+07 3.98e+03 4.25e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1236.63  200.    322.3   409.91  520.55]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[24525.34   200.01   300.     434.78   520.45]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[107717.31    200.9     300.      426.12    520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[70024.5    202.61   300.     419.56   520.14]\n",
      "\n",
      "pop_size: 33\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[7.39e+04 1.72e+06 3.20e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.90e+05 2.18e+02 3.00e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.62\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[38487.9    200.05   300.     426.09   520.14]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.95\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[3.02e+05 9.30e+06 4.50e+02 4.25e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[257.54 295.2  300.   426.09 520.31]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[114838.28 314543.25   1476.89    436.89    520.15]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.59e+06 4.82e+05 7.18e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.60e+06 3.00e+08 4.56e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5961.41  200.05  300.    409.8   520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[115.11 407.01 300.01 401.13 520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[335662.53   2334.82    802.43    417.88    520.18]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6.05e+05 2.23e+06 3.67e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.98\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[4784.41 1914.77  758.22  427.17  520.45]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.59\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3.22e+05 8.01e+03 3.12e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.73\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.79e+05 8.24e+04 3.69e+02 4.31e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.93\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.25e+06 1.32e+09 6.57e+03 5.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[109.88 825.18 300.   408.73 520.39]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[169783.6    2140.03    303.01    410.93    520.53]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.80e+07 7.02e+09 2.41e+04 9.00e+02 5.21e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1487.5   200.    300.    418.47  519.93]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3.16e+05 4.68e+02 3.01e+02 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[40969.59   205.58   300.     409.83   520.4 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[25003.51   200.     300.     426.09   520.15]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[82624.43   502.2    300.46   418.52   520.4 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.12e+07 4.76e+09 1.99e+04 1.17e+03 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.95e+07 2.89e+09 1.75e+04 7.51e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[118771.45   4089.06    301.43    418.62    520.49]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.99\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.68e+05 4.15e+06 3.38e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[2806.91 2743.63  300.06  411.06  520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    663.81    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[52237.67   217.55   300.01   426.11   520.39]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10672.3    200.    1044.07   417.39   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[7.39e+05 2.29e+02 4.75e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.55\n",
      "variant: 9 (best/2/bin)\n",
      "[143.   206.54 300.   434.78 520.58]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[27419.52   204.13   300.     411.55   520.17]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[7758.93  200.11  300.    402.17  518.54]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.60\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[122490.58    209.46    300.      410.71    520.14]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[494.2  200.01 300.   426.09 520.27]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.95\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5788.16 3259.71  694.5   432.23  520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.62\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[14034.18   200.01   300.     408.75   520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[230906.27   1053.1     301.67    409.9     520.42]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.37\n",
      "variant: 9 (best/2/bin)\n",
      "[4.36e+06 9.40e+07 4.92e+03 4.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 435.93 1633.48  305.65  418.7   515.34]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6009.63  200.    300.    426.09  510.34]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.80\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3566.14  200.03  300.    400.01  520.16]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.62\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[108708.47    220.14    300.      420.69    520.15]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[927.96 267.84 300.   428.26 520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[967.79 200.   300.93 427.17 520.03]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     422.28   516.19]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[225148.81    272.42    300.02    401.55    520.16]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6958.56  200.    355.34  417.39  520.12]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1508.79  645.84  300.    422.24  520.51]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[35325.99   252.35  3411.59   434.18   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.69\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[136205.51    208.05    300.      424.34    520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[10658.2    200.     300.     410.07   520.17]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[172281.88   2672.89    302.26    427.19    520.52]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[6.35e+07 6.25e+09 3.14e+04 1.84e+03 5.21e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[131.87 208.52 300.   427.17 520.54]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.99\n",
      "F: 0.38\n",
      "variant: 10 (rand/2/bin)\n",
      "[102.87 268.04 300.   417.4  520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6013.49  202.76  300.    411.95  518.38]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[2.02e+07 1.33e+08 5.18e+03 4.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.59\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.90e+06 3.24e+05 1.34e+03 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   415.21   517.97]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2416.34  200.    300.    411.69  520.38]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.93\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[19031.16   249.78   300.     417.39   520.35]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[15054.71   201.69  1036.24   425.3    519.86]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.60\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[2.29e+05 2.25e+02 3.00e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[1491.05  200.64  300.    418.47  520.27]\n",
      "\n",
      "pop_size: 45\n",
      "CR: 0.68\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[250717.43    687.24    300.23    409.91    520.21]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21414.55   200.     317.34   434.78   520.14]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.88\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4989.72  200.01  300.    426.09  520.36]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[41771.82   200.57  1160.52   416.46   520.16]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29972.26   200.01   300.     411.95   520.15]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[11649.38   200.17   300.     427.17   520.53]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.69e+07 3.13e+08 8.19e+03 7.91e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[914.53 200.   385.27 417.52 520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.82\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6046.09  200.    300.    408.7   520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1845.82  200.    306.95  418.48  520.49]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4986.66  200.    300.    434.78  520.47]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3980.96  200.    358.17  408.83  520.2 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.   300.   426.09 520.44]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[77144.06   212.09   300.     401.43   520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[116704.73    220.21    300.      417.95    518.19]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.55\n",
      "variant: 9 (best/2/bin)\n",
      "[585.82 200.   320.33 400.12 520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[17657.71   200.23   300.     417.42   520.55]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10074.1    200.01   300.     426.1    520.18]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1086.23  321.75  300.    409.94  520.59]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[12612.41   200.01   300.     418.53   520.19]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[104.27 388.21 300.   419.1  520.57]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.42e+05 2.42e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[7343.45 2870.14  300.8   427.22  520.24]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[152335.21   3421.51    302.59    418.61    520.45]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.73\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.16e+06 4.31e+08 7.00e+03 4.38e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[211926.4     919.29    300.82    418.51    520.55]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.46e+05 1.68e+07 3.71e+02 4.33e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[508.58 211.87 300.   418.47 520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[141445.75    572.39    300.53    408.85    520.21]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.65\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.10e+05 2.47e+03 3.03e+02 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[2.22e+07 3.76e+09 1.38e+04 8.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.92\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[1158.06  200.68  300.    426.09  520.48]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[13860.88   287.37   438.63   409.82   520.19]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[112.07 396.86 300.   409.81 520.47]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.29e+06 8.67e+04 5.61e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[6.06e+06 3.45e+07 2.58e+03 4.49e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1.04e+07 1.71e+08 1.45e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.83\n",
      "F: 0.65\n",
      "variant: 4 (best/2/exp)\n",
      "[4.80e+05 3.20e+05 3.35e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[4.05e+07 2.75e+09 2.70e+04 7.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[12216.09  2500.5    302.56   417.54   520.33]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[5.18e+05 1.81e+07 1.40e+03 4.80e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[41925.8    200.     300.     418.47   520.14]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.83\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[4807.69  200.03  300.    417.39  520.29]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.55\n",
      "variant: 4 (best/2/exp)\n",
      "[3672.12 1117.    685.52  409.85  520.24]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[10698.65   200.87   331.96   417.78   520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[3507.79  200.    300.    418.47  520.28]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[29502.28   200.     300.     410.86   520.05]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10988.39   200.03   300.     434.78   520.17]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.32e+05 2.89e+04 3.29e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.50e+07 1.85e+09 1.54e+04 1.46e+03 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[23944.     200.04   300.     402.17   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[55467.72  1798.56   397.59   425.48   516.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.45e+06 2.75e+09 1.17e+04 5.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[112771.86    591.6     300.45    426.13    520.41]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[196.78 200.02 300.   427.17 520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.73\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4125.58 1092.31  672.38  409.37  520.56]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.98\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.15e+06 4.89e+05 3.06e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 10 (rand/2/bin)\n",
      "[3.55e+05 1.64e+03 3.01e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4967.51  200.01  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.60\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[41469.71   201.8    300.     409.84   520.12]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.54e+06 2.53e+07 1.82e+03 4.65e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.67e+06 1.21e+07 4.90e+03 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.91\n",
      "F: 0.55\n",
      "variant: 9 (best/2/bin)\n",
      "[1142.58  573.34  300.01  426.11  520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[8.72e+06 5.21e+09 5.66e+03 6.89e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.16e+05 3.62e+04 3.18e+02 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 7980.74 22183.42   300.52   410.     520.35]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[251.05 550.13 300.   426.09 520.31]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.10e+07 5.81e+08 5.94e+03 4.85e+02 5.21e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[121708.87    413.96    300.31    426.09    520.28]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[2397.69  200.01  300.    417.6   520.31]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[469.45 919.55 300.01 426.09 520.29]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.25e+07 1.66e+09 1.56e+04 5.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[140.99 200.01 300.   408.86 520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3224.63  200.    300.    434.78  520.41]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 161.04 1321.42  300.01  417.54  520.55]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[3.09e+05 2.32e+02 3.00e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[45252.71  1090.75   797.88   417.54   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[174640.31    213.18    300.      400.3     520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[140591.02   3092.03    723.07    429.33    520.18]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[10506.26   200.6    300.     418.48   520.2 ]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1139.11  200.    300.    418.47  520.18]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[133505.29    203.84    300.      409.97    520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1447.6   200.    301.21  418.53  520.53]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[14900.31   200.01   300.     408.7    520.16]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1996.14  200.    300.    417.42  520.44]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.68\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[199911.62    264.52    300.01    411.27    520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.20e+04 1.51e+06 3.10e+02 4.13e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1363.01  200.    300.04  418.61  520.47]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[66460.96   203.53   300.     401.16   520.16]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[109.88 825.18 300.   408.73 520.39]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.62\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.98e+05 5.88e+07 5.42e+02 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.82\n",
      "F: 0.46\n",
      "variant: 9 (best/2/bin)\n",
      "[3.40e+06 1.65e+09 5.02e+03 5.79e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.82e+04 4.88e+06 3.27e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.59e+06 3.54e+08 1.23e+03 4.61e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.17 200.   300.   426.09 520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.62\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1178.61  200.    570.8   426.24  520.38]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+07 1.71e+09 2.25e+04 7.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.10e+06 1.36e+08 5.00e+03 4.75e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.45\n",
      "variant: 4 (best/2/exp)\n",
      "[187539.23   2808.4     303.64    419.37    519.43]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[851.76 200.   300.   426.09 520.41]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2797.98  201.67  300.    437.54  520.54]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[789.51 289.1  300.   426.15 520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.60\n",
      "F: 0.23\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.41e+06 8.28e+03 4.03e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[24053.28   765.73  1075.04   431.54   520.51]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.97e+05 1.98e+08 1.45e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.20e+04 1.51e+06 3.10e+02 4.13e+02 5.21e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.60\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[114168.49    210.31    300.      417.44    520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1675.77  206.35  318.4   427.17  520.46]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 206.57 1154.89  300.01  417.51  520.46]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[753.27 200.   300.   409.78 520.5 ]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[499.52 200.   300.   418.47 520.44]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[162357.17   1391.71    301.19    409.88    520.55]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.69e+07 1.24e+09 2.53e+04 4.92e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.60e+06 3.00e+08 4.56e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[1.71e+06 1.18e+06 9.69e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.66\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[167549.78    217.5     300.      422.53    520.17]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[49676.79   200.11  1144.42   426.09   520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[59254.13  1855.86   303.69   418.15   520.24]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.73e+07 1.11e+09 8.08e+03 5.12e+02 5.21e+02]\n",
      "\n",
      "pop_size: 66\n",
      "CR: 0.67\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[7.93e+05 6.71e+04 5.09e+02 4.06e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.97e+05 1.98e+08 1.45e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5961.41  200.05  300.    409.8   520.53]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.76\n",
      "variant: 4 (best/2/exp)\n",
      "[42747.78   201.1    300.     401.85   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6417.95  200.    300.    418.47  520.14]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4.65e+05 3.15e+04 3.25e+02 4.29e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3.00e+07 3.86e+09 1.36e+04 6.54e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[3.71e+06 1.28e+09 2.64e+03 5.91e+02 5.21e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.54\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[7.66e+05 4.05e+03 3.93e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[9.76e+05 4.04e+04 3.55e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 1.00\n",
      "F: 0.71\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.35e+07 2.16e+09 2.49e+04 7.62e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21414.55   200.     317.34   434.78   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[1.66e+07 1.14e+09 9.24e+03 5.65e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.80\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[9.80e+05 7.43e+06 5.71e+02 4.35e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 10 (rand/2/bin)\n",
      "[7.97e+03 1.58e+06 3.19e+02 4.06e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[4.66e+06 7.79e+07 3.96e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[112061.75    249.02    300.04    403.77    520.17]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.60\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[2.05e+06 3.24e+06 1.22e+03 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1.34e+07 9.74e+07 1.29e+04 4.79e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1117.1   200.01  367.67  410.14  520.48]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[112.12 211.96 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[428.23 291.7  300.   419.56 520.37]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[6.01e+05 3.09e+07 4.83e+03 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[11827.3  32040.17   301.13   410.07   520.53]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1131.69  200.    300.    418.47  519.36]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4866.75  200.    300.    419.56  520.16]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.61\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[9288.04  205.41  697.17  411.1   520.51]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[112.12 211.96 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.65\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[53537.75   200.73   594.47   426.09   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.90e+05 2.87e+02 3.00e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[54454.25   517.     300.03   434.78   520.24]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[5.30e+06 7.78e+05 7.94e+02 4.29e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.66\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[2.65e+05 2.30e+02 3.00e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1098.55  200.06  300.    426.36  520.33]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5198.36  200.    300.    417.4   520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.58\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[267806.25    318.97    300.57    410.4     520.11]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2586.52  200.    470.1   427.17  520.43]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[100.48 200.   300.   426.09 520.49]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[671.06 370.72 300.   417.39 520.58]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[151.31 200.01 300.   409.78 520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[23866.54  4405.08   605.37   419.61   520.31]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.66\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[125900.4    1991.97    301.81    402.65    520.19]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[9566.65  200.01  300.    408.99  520.11]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.72\n",
      "variant: 10 (rand/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.61e+06 3.75e+08 1.50e+03 4.90e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[65958.69   238.94   300.03   426.14   520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[12444.16   200.     719.51   434.78   520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101505.54    906.73    300.89    427.17    520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[126381.63  90305.42   1677.85    428.17    520.47]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1488.33  212.85  300.    434.97  520.58]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4331.14  200.03  300.    418.47  520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4.45e+06 2.26e+08 5.13e+03 5.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[230906.27   1053.1     301.67    409.9     520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.71e+06 2.00e+07 3.70e+03 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.67\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[203256.42   1081.31    301.15    417.54    520.18]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3.08e+05 1.03e+07 1.41e+03 4.19e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[162.02 212.59 300.   417.39 520.36]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[11485.57   200.     305.69   409.78   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.87e+05 1.16e+05 3.24e+02 4.24e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.42\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 123.56 3916.74  300.03  417.44  520.52]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[133429.4     211.53    300.02    426.13    520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1180.21 2559.13  305.67  419.85  520.49]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[43518.27   200.23   300.     401.1    520.14]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[3.78e+05 1.82e+06 3.99e+02 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.82\n",
      "F: 0.40\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.54e+07 4.05e+09 2.46e+04 1.29e+03 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[135889.35    201.38    300.      427.17    520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[5801.    200.02  300.    418.5   520.5 ]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   212.15 300.   418.47 520.52]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[6.21e+06 3.17e+03 6.64e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[60809.07  4044.69  1482.91   438.23   520.43]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[102.31 200.05 300.   417.39 520.61]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.71\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[6.68e+06 1.19e+08 8.21e+03 4.50e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.98\n",
      "F: 0.49\n",
      "variant: 4 (best/2/exp)\n",
      "[230.93 200.13 300.   427.17 520.44]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[29623.41  4045.64   949.17   410.08   520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4967.51  200.01  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[2443.14 4439.46  300.17  412.12  520.38]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.73e+05 3.00e+06 7.17e+02 4.35e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2245.38  200.    398.85  427.21  520.43]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6367.58  767.75 2413.95  435.44  520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.10e+06 3.60e+06 8.32e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[11563.62   200.73   300.     418.48   520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[56476.75   200.01   300.     408.93   520.15]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[12216.09  2500.5    302.56   417.54   520.33]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[18837.83   200.     300.     402.17   520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[5.65e+04 2.86e+07 6.67e+03 4.30e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[429.27 402.4  300.01 408.7  520.32]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[93118.91   208.92  1287.71   424.64   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10534.63   200.     300.     426.09   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[183346.56    232.26    300.      411.18    520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3.52e+07 5.98e+09 2.63e+04 8.51e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[8.05e+05 3.06e+04 3.35e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[1.97e+06 2.01e+06 7.07e+02 4.29e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[2.67e+06 1.21e+07 4.90e+03 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[116.87 200.72 300.   427.17 520.36]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   418.47   517.66]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[7.89e+05 8.77e+05 4.09e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.31e+07 3.03e+09 1.20e+04 2.26e+03 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[96037.55   953.14   300.65   417.45   520.48]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.17 200.   300.   426.09 520.57]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.99\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[9.39e+06 9.65e+06 4.05e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.83\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[15377.55  1274.37   300.04   434.78   520.26]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[23981.58  1077.47   645.99   420.15   520.12]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.96e+05 2.15e+04 3.24e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.91\n",
      "F: 0.71\n",
      "variant: 9 (best/2/bin)\n",
      "[376.18 201.29 300.   418.48 520.44]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[140.99 200.01 300.   408.86 520.52]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2.60e+05 6.26e+05 1.33e+03 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[107.32 200.02 300.   417.39 520.47]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[49790.07   203.57   303.64   419.6    520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[19348.39   200.14   300.     417.4    520.44]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[251.85 359.57 300.   417.39 520.33]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8638.94  200.    300.    400.    520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1472.52  200.    300.    418.47  520.29]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[877.87 584.4  316.72 417.86 520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4414.33  200.    300.    419.56  520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.49e+07 2.79e+08 5.25e+02 4.96e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[174640.31    213.18    300.      400.3     520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1.96e+07 4.90e+09 1.03e+04 8.67e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.91e+05 6.32e+04 3.30e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 435.93 1633.45  305.65  418.81  515.34]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.93\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[75743.33  4799.75   312.25   433.13   520.59]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1164.1   200.    300.23  418.58  520.44]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3769.03  200.    300.    434.78  520.56]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 107.85 1881.25  300.11  409.82  520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[41613.14   200.     300.     418.47   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[31224.48   200.22   323.74   430.69   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[91179.49   200.36   300.     409.82   520.16]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[109.88 825.18 300.   408.73 520.39]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 501.28 1124.4   300.    401.09  520.5 ]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.68\n",
      "F: 0.74\n",
      "variant: 4 (best/2/exp)\n",
      "[39510.11   200.04   300.     400.42   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6804.12  200.    300.    426.09  520.16]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[270355.56   5616.75    305.49    427.21    520.41]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.73e+07 8.93e+08 1.79e+04 6.38e+02 5.21e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.70\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[26009.55   243.19   300.01   411.01   520.24]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.95\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[44570.76  4320.14   522.63   427.17   520.32]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.80\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[16012.63   202.36   300.     417.4    520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.62\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[190905.27    203.36    300.      402.29    520.16]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[102.1  748.52 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[7.82e+06 1.22e+06 2.51e+03 4.46e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[141445.75    572.39    300.53    408.85    520.21]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1888.87  200.   2570.51  418.52  520.39]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.94\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5871.58 2948.42  318.86  418.51  520.55]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[21175.89  2050.22   373.31   419.91   520.21]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.65\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[54624.39   200.02   300.     401.09   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[14230.45   207.53  1119.7    418.55   520.28]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[169783.6    2140.03    303.01    410.93    520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[106313.47    667.98    300.58    426.11    520.23]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1634.44  200.85  300.    427.26  520.52]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.59\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[57080.76   200.12   300.     417.43   520.44]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[554.28 836.21 300.02 418.48 520.37]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.49\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[249620.55    511.1     308.31    419.71    520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[39145.31   200.01   300.     418.47   519.97]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.36\n",
      "variant: 4 (best/2/exp)\n",
      "[1476.96  200.17  300.    434.78  520.3 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[314.59 406.94 300.   401.09 520.32]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[154325.63    663.81    300.27    418.68    520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.92\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.36e+05 4.61e+04 6.95e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[ 535.84 3609.88  300.    401.13  520.44]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.74\n",
      "variant: 4 (best/2/exp)\n",
      "[2.37e+05 2.27e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      "\n",
      "pop_size: 60\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[223313.3    3248.97    304.43    419.77    520.24]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8.71e+06 1.50e+09 1.39e+04 4.88e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[151579.89    206.18    300.      402.56    520.08]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[4.02e+05 3.42e+02 3.00e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[17249.38   200.     359.64   418.47   520.18]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[711.45 200.09 300.   434.78 520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4424.91  200.63  309.43  427.25  520.49]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[3.93e+05 1.16e+07 4.09e+02 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[33391.35   200.24   300.     426.22   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[116704.73    220.21    300.      417.87    518.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.98\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.23e+07 2.94e+09 1.42e+04 6.18e+02 5.21e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.69\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[1.73e+06 1.08e+06 1.08e+03 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.81e+07 1.56e+09 1.47e+04 8.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.81e+05 1.91e+04 3.80e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[2.02e+06 9.06e+05 1.21e+03 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.27e+07 1.73e+09 1.46e+04 6.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[329.33 201.88 300.   418.47 520.55]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[7.86e+07 1.08e+10 1.90e+04 1.77e+03 5.21e+02]\n",
      "\n",
      "pop_size: 69\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.18e+05 4.95e+04 3.90e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.10e+05 3.47e+06 4.55e+02 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.66\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[1.94e+06 5.30e+05 1.04e+03 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[9.73e+05 5.61e+06 5.09e+02 4.30e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1.19e+07 1.71e+09 2.25e+04 7.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.48e+06 2.28e+08 1.38e+03 4.73e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     426.09   516.19]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.12e+07 4.76e+09 1.99e+04 1.17e+03 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[6.35e+05 2.13e+04 3.22e+02 4.04e+02 5.19e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10672.3    200.    1044.07   417.39   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1290.35  200.    366.2   408.88  520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2128.31  200.    359.    435.6   520.47]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3980.96  200.    358.17  408.83  520.2 ]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[957.47 286.52 300.   426.09 520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[ 781.22 3951.67  300.    426.41  520.28]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.59\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[64270.25   202.41   300.     418.57   520.16]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.92\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[1.23e+05 3.29e+06 3.52e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[3296.87  200.    300.    418.47  520.44]\n",
      "\n",
      "pop_size: 42\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[56682.82   227.03   300.     434.78   519.27]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[3.93e+05 1.36e+03 3.01e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5198.36  200.    300.    417.4   520.21]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19443.67   200.     300.     418.56   520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[19809.71   200.     300.     426.09   520.18]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.75\n",
      "variant: 9 (best/2/bin)\n",
      "[82501.33   440.04   300.08   434.78   520.43]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[8.56e+06 3.02e+08 4.32e+03 4.74e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[232162.81    772.69    300.28    417.42    519.42]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[482.47 200.4  300.   426.09 520.47]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.34\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.51e+05 9.79e+04 1.31e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.78\n",
      "variant: 4 (best/2/exp)\n",
      "[1.23e+06 9.54e+06 8.16e+02 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[56400.1    201.02   300.     408.74   520.5 ]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[94799.89   200.     300.     401.25   520.12]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.60\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[89249.43   200.2    300.     417.41   520.13]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[344.77 216.   300.   418.48 520.36]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[65958.69   238.94   300.03   426.14   520.55]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3741.11  200.03  300.    433.69  520.32]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6013.49  202.82  300.    411.95  518.38]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.42e+05 2.42e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.74\n",
      "variant: 9 (best/2/bin)\n",
      "[2.69e+06 1.43e+07 1.80e+03 4.38e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[44080.31   628.79   300.01   409.95   520.14]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[49830.13   202.76   300.02   422.76   517.43]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 9641.16 11394.44   300.47   403.35   520.33]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[9.68e+06 9.93e+08 9.78e+03 7.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[10506.26   200.6    300.     418.48   520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[82226.34   214.9    300.02   401.81   520.15]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[3.47e+05 1.45e+04 3.08e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.05e+07 3.16e+09 1.80e+04 6.71e+02 5.19e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4967.51  200.01  300.    418.47  520.18]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.74\n",
      "variant: 9 (best/2/bin)\n",
      "[133134.75    210.81    300.02    417.42    520.55]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   511.61 300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[ 225.81 1886.49  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1363.01  200.    300.04  418.61  520.47]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[33075.61   200.91   372.4    419.66   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.01e+07 6.52e+08 1.05e+04 5.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[73742.48   366.43   300.17   419.6    520.2 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[105.67 200.09 300.   417.39 520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[624.52 702.85 300.   426.37 520.52]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.65\n",
      "F: 0.70\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.30e+06 4.36e+06 9.82e+03 4.63e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.93\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[923.6  200.95 300.   417.39 520.38]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[120.78 202.93 300.   419.56 520.46]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1979.1   200.    340.94  427.24  520.58]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6282.73  201.08  310.72  426.09  520.13]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.91\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[12165.4    200.55   300.     417.39   520.3 ]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.68\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[96315.66   872.78   300.71   419.57   520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[133429.4     211.53    300.02    426.13    520.45]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1702.38  200.04  300.    418.47  520.29]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   418.47   517.66]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1.13e+06 9.20e+06 4.57e+02 4.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[174.61 221.05 300.   427.17 520.09]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[1.44e+06 4.37e+04 3.90e+02 4.17e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[5.86e+05 1.28e+04 3.08e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.99\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[41970.92  2562.88   300.06   410.86   520.41]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3548.97  200.    300.    426.09  520.56]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[9802.79  200.    353.06  410.02  520.42]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.67e+07 2.76e+09 1.01e+04 1.18e+03 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[163159.49   8643.02    307.09    426.49    520.25]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.57\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[57221.47   202.49   300.01   434.78   520.49]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2301.93 5651.55  985.32  409.89  520.52]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[3.06e+05 2.64e+03 3.03e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1.33e+06 1.36e+07 3.34e+03 4.68e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10010.29   200.     306.72   400.34   520.15]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.47e+05 2.11e+04 3.12e+02 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.26e+07 2.22e+09 2.26e+04 5.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[4.33e+04 8.42e+06 3.44e+02 4.26e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.56\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[8155.66  200.    300.    418.49  520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[12612.41   200.01   300.     416.63   520.19]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[1281.19  200.    300.    434.78  520.34]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[231312.72   2602.17    302.4     426.18    520.44]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.06e+07 1.16e+09 2.61e+04 2.19e+03 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[25249.27   200.44   300.38   426.27   520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.84\n",
      "F: 0.49\n",
      "variant: 4 (best/2/exp)\n",
      "[1459.69  309.03  300.    426.1   520.29]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.98\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[87690.54  4764.98   365.62   426.09   520.37]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2.77e+06 3.58e+07 2.90e+03 4.37e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.93\n",
      "F: 0.75\n",
      "variant: 9 (best/2/bin)\n",
      "[1.16e+06 1.04e+08 8.50e+02 4.59e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.65\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[24863.38   200.39   300.     409.93   520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.97\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.88e+04 1.08e+07 1.17e+03 4.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.70e+05 5.78e+04 3.31e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[14676.22   200.     300.     404.38   520.11]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   200.02 300.   434.78 520.45]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.61\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[72225.85   202.52   300.     402.4    520.13]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6.78e+05 7.38e+02 3.06e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2328.86  212.41  300.    418.94  520.47]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[11042.47   200.     300.     410.06   520.17]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[115.11 407.01 300.01 401.13 520.49]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[2.39e+05 5.42e+06 1.37e+03 5.52e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[12127.7    200.08   300.     417.57   520.56]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[8.67e+05 8.58e+06 3.35e+03 4.24e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[153708.05   1338.4    2400.45    427.51    520.19]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.42\n",
      "variant: 9 (best/2/bin)\n",
      "[3.00e+05 3.16e+07 3.87e+02 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4866.75  200.    300.    419.56  520.16]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[8251.7   200.    313.26  426.24  520.42]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.88\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[1562.33  785.63  300.    426.09  520.52]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.73\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.42e+06 2.09e+08 4.25e+03 4.52e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.44e+06 9.52e+04 4.47e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.57\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1283.94  200.    312.22  418.58  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.14e+07 1.79e+09 7.93e+03 6.97e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[199109.61    208.46    300.03    401.73    520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1577.2   200.    341.82  419.58  520.54]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.21e+06 3.45e+07 6.07e+03 4.49e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[230906.27   1053.1     301.67    409.9     520.42]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.89\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[15761.5    244.36   302.78   418.49   520.19]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[62175.89   661.68   300.27   417.45   520.27]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1236.63  200.    321.08  409.91  520.55]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[102.31 200.05 300.   417.39 520.61]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.74\n",
      "variant: 4 (best/2/exp)\n",
      "[2.37e+05 2.27e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[22885.5   4249.4   3477.77   410.16   520.25]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[45909.41   394.3    300.06   418.49   519.99]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[259.54 200.   300.   417.39 520.44]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.57\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.14e+06 4.67e+08 5.03e+03 4.84e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.45e+05 3.12e+02 3.00e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.95\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[506.63 200.92 300.13 426.12 520.41]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.03e+05 6.56e+06 3.78e+03 4.28e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[108.29 213.55 300.   427.17 520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3019.61  200.    300.    418.47  520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.87\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[689.66 205.46 300.   409.78 520.53]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.00e+06 7.08e+08 1.34e+04 7.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[17454.1  98466.77   302.12   410.03   520.3 ]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[4.49e+05 1.54e+04 1.02e+03 4.41e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2529.21  200.    300.    417.43  520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[140.99 200.01 300.   408.86 520.52]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6552.74  200.    300.    409.91  520.13]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[848.32 200.   300.   417.4  520.44]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1176.82 1288.5   300.75  434.78  520.59]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[40149.28   200.04   300.     400.03   520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[2.78e+05 2.31e+02 3.00e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1.04e+07 5.79e+08 1.02e+04 4.89e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[5.22e+05 3.52e+04 3.24e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.67\n",
      "variant: 9 (best/2/bin)\n",
      "[1.60e+04 1.97e+06 3.11e+02 4.25e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[228.   233.05 300.   409.78 520.32]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[8.46e+05 2.46e+05 1.89e+03 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.73\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.54e+07 7.34e+08 7.27e+03 5.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 206.57 1154.89  300.01  417.51  520.46]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[707.26 204.07 300.   434.82 515.38]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1500.5   429.29  300.    417.39  520.36]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[8.16e+06 5.82e+06 1.04e+04 4.68e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[46459.5    210.83   307.65   408.82   520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[47541.35   200.     300.     409.81   520.44]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.19e+05 2.70e+02 3.04e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[9.15e+04 9.01e+05 1.04e+03 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[33024.43 71140.64   303.66   402.96   520.35]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[881.24 200.   307.68 409.09 520.38]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.73e+05 3.06e+04 3.22e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+06 9.95e+08 6.75e+03 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.74\n",
      "variant: 4 (best/2/exp)\n",
      "[42465.35   200.34   300.     402.34   520.17]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1819.38 1925.32  300.    438.49  520.57]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.92\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.44e+06 3.41e+03 6.43e+02 4.59e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.59\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[283918.77   3651.97    316.93    417.55    520.45]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.49\n",
      "variant: 9 (best/2/bin)\n",
      "[101.64 200.   300.   409.78 520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[76670.83   203.12   300.     403.32   520.16]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[951.91 200.86 300.   409.78 520.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[140.99 248.15 300.   408.86 520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[23328.93   200.     300.04   417.41   520.21]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.89\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[646.59 200.02 300.   427.17 520.03]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[83153.35   546.61   300.09   409.94   520.26]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[44263.32   200.     300.41   427.24   520.43]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[ 127.87 1385.62  300.    417.39  520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[31989.96   691.78   361.44   411.04   520.43]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1590.4   200.    300.46  409.92  520.39]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[14534.96   200.13   300.     409.8    520.41]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 790.54 2900.78  300.7   435.71  520.46]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6.55e+06 4.45e+08 8.99e+03 5.20e+02 5.17e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[94759.09  1445.47   300.45   426.14   520.4 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[72063.32   202.24   300.     408.88   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2940.04  200.    307.9   418.49  520.44]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   200.01 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[ 535.84 3609.88  300.    401.13  520.44]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.88\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[971.52 200.   300.   426.09 520.2 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19973.18   200.     300.     417.52   520.15]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[7758.93  200.11  300.    402.17  518.54]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.88e+05 8.12e+05 3.92e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3.07e+06 2.68e+08 9.06e+02 4.57e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[153945.43    212.02    300.      417.46    520.16]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.60\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[12625.96   200.04   300.     426.11   520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[1.44e+07 2.53e+09 1.75e+04 7.21e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[149286.45    924.6     301.09    426.12    520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[6.26e+07 4.07e+09 2.92e+04 9.57e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[3769.03  200.    300.    434.78  520.56]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[7174.71  200.    311.88  418.63  520.42]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[431.57 362.92 300.   426.09 520.33]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[8.74e+05 1.07e+04 2.52e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.65\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[22802.     200.34   304.05   434.15   520.11]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.93\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[4293.86 7615.31  300.52  410.92  520.42]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.59\n",
      "F: 0.71\n",
      "variant: 4 (best/2/exp)\n",
      "[133466.03    218.33    300.      418.5     520.12]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.87\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[1498.81  200.    300.    410.86  520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[ 100.18 1363.79  300.    427.17  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[176754.21    232.63    300.      411.18    520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[9.38e+05 1.95e+08 2.51e+03 4.99e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.47e+05 4.69e+06 2.84e+03 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[9.46e+06 2.92e+09 9.98e+03 8.55e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.54\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[1.91e+06 2.71e+05 1.47e+03 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[9.74e+05 4.16e+08 2.53e+03 4.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.44\n",
      "variant: 9 (best/2/bin)\n",
      "[313825.42   7525.11    360.72    438.33    520.37]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[2.09e+06 5.05e+05 5.88e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 5 (rand/2/exp)\n",
      "[17758.45 23618.73   300.72   411.27   520.32]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.72e+06 5.98e+07 2.96e+03 4.45e+02 5.21e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.60\n",
      "F: 0.38\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.91e+06 7.88e+07 5.76e+02 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[7.14e+05 4.03e+07 5.36e+02 4.48e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[14676.22   200.     300.     404.38   520.11]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[3.34e+07 4.00e+09 2.82e+04 7.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.15e+06 5.09e+05 5.44e+02 4.11e+02 5.19e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[116704.73    220.21    300.      417.87    518.19]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[31303.99   200.01   300.     434.78   520.41]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[106244.47    258.09    300.      418.8     520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.73\n",
      "variant: 4 (best/2/exp)\n",
      "[219159.85    272.42    300.02    401.72    520.16]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 1.00\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[5.33e+07 4.74e+09 2.37e+04 1.37e+03 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[3.55e+05 1.60e+04 3.18e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.54\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[72346.52   214.66   300.58   418.63   520.15]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[1206.73  200.    721.72  434.78  520.52]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[2.85e+08 7.63e+09 1.59e+04 2.93e+03 5.21e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[5.45e+05 3.94e+04 3.97e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6289.73  201.08  310.72  426.09  520.13]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[21779.81   200.     675.83   434.78   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.63\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[39453.55   200.01   300.     409.8    520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   285.26 300.   426.09 520.54]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.03e+05 2.10e+04 3.20e+02 4.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[3.13e+05 3.20e+02 3.00e+02 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 9641.16 11394.44   300.47   403.35   520.33]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3004.03  200.    313.6   401.26  520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[190708.65    214.08    300.      409.97    520.12]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1.58e+07 4.97e+09 1.55e+04 5.77e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[101505.54    895.82    300.89    427.17    520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 501.28 1124.4   300.    401.09  520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[19115.83   200.     300.     417.4    520.15]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.60\n",
      "F: 0.70\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[10151.83   249.9    300.     411.04   520.18]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.12e+05 5.16e+03 3.10e+02 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.13e+07 3.45e+09 1.30e+04 8.88e+02 5.20e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 10 (rand/2/bin)\n",
      "[1.79e+06 4.23e+06 1.07e+03 4.35e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.81e+08 6.92e+09 2.18e+04 1.67e+03 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.65\n",
      "F: 0.68\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[57107.32   294.08   300.42   426.14   520.52]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.93\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[1.38e+07 1.18e+08 1.11e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.61\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[68578.22   206.73   300.     409.8    520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[60173.25   200.12   300.     401.17   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4210.8   200.    300.89  409.02  520.37]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.61e+07 6.39e+09 1.99e+04 6.90e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.68\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[111798.36   1384.71    300.58    426.12    520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[51312.41   621.31   300.06   418.53   520.27]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.96\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[5.45e+06 3.79e+08 5.08e+03 5.22e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.92\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.26e+06 3.94e+07 8.10e+02 4.19e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.68\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[136985.59   1525.03    300.53    409.23    520.26]\n",
      "\n",
      "pop_size: 65\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[128.97 201.18 300.   409.78 520.51]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.68\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[2.55e+06 5.42e+07 6.41e+03 5.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 59\n",
      "CR: 0.89\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.19e+05 3.40e+06 4.67e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.56\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.06e+07 2.26e+09 1.76e+04 5.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[125623.63    200.47    300.      417.42    520.12]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.99\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[3.66e+04 3.00e+06 3.17e+02 4.30e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[2.35e+05 2.30e+02 3.00e+02 4.26e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[102725.89    853.34    304.17    419.95    519.77]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.37e+06 4.90e+08 1.14e+04 5.15e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   417.39   517.66]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[957.47 286.52 300.   426.09 520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.89\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.07e+05 1.04e+06 3.69e+02 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.57\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[6024.67  491.95  358.69  433.35  520.4 ]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[100927.9     582.63    300.33    427.2     520.24]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[5.30e+06 7.78e+05 7.94e+02 4.29e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1447.6   200.    301.21  418.53  520.53]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.94\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1001.93  207.25  300.    417.39  520.39]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2529.21  200.    300.    417.43  520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2351.73  200.    300.    409.79  520.38]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.89\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[466.29 203.02 300.   425.13 520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.56\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[2.52e+05 2.52e+02 3.00e+02 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[101.86 640.15 300.   426.09 520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[5029.29  200.    314.14  417.47  520.24]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.63\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[12482.89   200.     300.29   426.09   520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1845.82  200.    306.95  418.48  520.49]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10672.3    200.    1044.07   417.39   520.13]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[225.53 659.71 300.   426.09 520.44]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[16555.24   200.     300.     418.48   520.2 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.97\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[3.28e+07 8.55e+08 1.50e+04 4.71e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.57\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[2.24e+06 3.43e+06 1.34e+03 4.32e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.38e+06 4.55e+08 1.69e+03 4.79e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[3152.84  200.    344.8   410.98  520.49]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 435.93 1633.45  305.65  418.81  515.34]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[174.61 221.05 300.   426.22 520.09]\n",
      "\n",
      "pop_size: 61\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1508.79  645.84  300.    422.24  520.51]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.99\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[6.97e+05 1.35e+05 1.03e+03 4.33e+02 5.21e+02]\n",
      "\n",
      "pop_size: 57\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[511.88 200.29 300.   426.09 520.58]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[ 535.84 3609.88  300.    401.13  520.44]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[103.02 200.14 300.   417.4  520.5 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.97e+06 2.21e+09 7.28e+03 5.52e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.55\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[8.84e+05 2.99e+04 3.76e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[1.37e+06 2.33e+05 1.04e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.74e+06 2.28e+08 5.03e+03 4.62e+02 5.16e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[ 781.22 3951.67  300.    426.41  520.28]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.75\n",
      "variant: 4 (best/2/exp)\n",
      "[47834.08   202.75   300.     409.03   520.13]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.49\n",
      "variant: 4 (best/2/exp)\n",
      "[21958.05   200.     300.     434.78   520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[20823.16  1732.32   503.67   426.23   520.56]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[39145.31   200.01   300.     418.47   519.97]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 800.8  2094.56  304.81  434.78  515.97]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.71\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[55211.13   229.55   300.74   434.93   520.18]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.89\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[583.12 242.39 300.   418.48 520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[44263.32   200.     300.41   427.24   520.43]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[47155.81   546.1    300.06   419.57   520.22]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[ 284.99 1736.67  300.    402.17  520.47]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[ 790.54 2900.78  300.7   435.71  520.46]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[2.49e+04 6.97e+06 3.25e+02 4.18e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[7.15e+06 8.80e+08 1.54e+04 9.53e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[102.1  748.52 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 5 (rand/2/exp)\n",
      "[19922.14 35021.62   301.45   403.98   520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10655.55   200.     300.     400.12   520.11]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.72\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3349.75  200.    301.55  434.98  520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.73\n",
      "variant: 9 (best/2/bin)\n",
      "[70447.95   200.67   300.01   418.48   520.48]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10534.63   200.     300.     426.09   520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.89\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.33e+04 1.01e+07 1.07e+03 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.71\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[93483.27   831.79   300.09   409.8    519.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.59\n",
      "F: 0.71\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[28186.89   200.     316.26   401.46   520.46]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.42\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 44888.73 161018.22    303.39    411.01    520.3 ]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[51856.48   830.16   797.19   435.3    520.41]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2328.86  212.41  300.    418.94  520.47]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[52071.27  1229.1    355.73   449.65   520.12]\n",
      "\n",
      "pop_size: 40\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[211.33 200.61 300.   417.39 520.38]\n",
      "\n",
      "pop_size: 43\n",
      "CR: 0.59\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[1.68e+06 3.45e+06 1.11e+03 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 17\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3.97e+06 4.18e+08 6.37e+03 6.24e+02 5.21e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[5.67e+05 9.36e+07 2.26e+03 4.24e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2864.29  200.02  300.    426.09  520.21]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[270544.65 330571.54    834.96    451.07    520.18]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[29623.41  4045.64   949.17   410.08   520.42]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.43\n",
      "variant: 4 (best/2/exp)\n",
      "[1125.37  200.    300.    418.47  520.35]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.66\n",
      "variant: 4 (best/2/exp)\n",
      "[14268.13   200.     300.     426.09   520.17]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3548.97  200.    300.    426.09  520.56]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[10649.73   200.     399.2    417.5    520.47]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.48\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[226193.42    297.24    301.79    415.37    520.12]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.01e+04 3.73e+06 3.17e+02 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40149.28   200.04   300.     400.03   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[15929.21   200.     874.76   402.27   516.97]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3019.61  200.    300.    418.47  520.5 ]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19973.18   200.     300.     417.52   520.15]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[9.20e+05 2.44e+04 3.31e+02 4.15e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[9.13e+06 2.28e+09 1.73e+04 7.96e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[8.10e+05 6.27e+05 4.85e+02 4.54e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.98\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.45e+06 2.75e+09 1.22e+04 5.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[279772.38  27255.36    313.46    412.44    520.24]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.55\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[1.25e+06 3.90e+05 1.29e+03 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[2.31e+07 1.17e+09 2.24e+04 6.39e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[31303.99   200.01   300.     434.78   520.41]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[51474.26   200.12   300.65   417.42   520.12]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21414.55   200.     317.34   434.78   520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.98\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[167.83 200.   300.   426.09 520.43]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.55\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[48657.29   201.29   300.46   410.18   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 9 (best/2/bin)\n",
      "[52237.67   217.55   300.01   426.11   520.39]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[684.25 200.01 300.   434.78 520.31]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1236.63  200.    322.3   409.91  520.55]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[138471.77   1636.42    301.52    417.5     520.48]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[1.99e+07 3.16e+09 1.80e+04 6.71e+02 5.19e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3741.11  200.03  300.    434.78  520.32]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3655.28  200.02  300.    408.7   516.35]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4358.19  200.    300.    418.52  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[9.46e+06 3.81e+09 2.17e+04 1.03e+03 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2.18e+06 5.19e+05 7.27e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[17096.88   200.    1036.28   417.69   520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[3.97e+05 4.80e+02 3.01e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.32\n",
      "variant: 9 (best/2/bin)\n",
      "[6.41e+06 1.30e+09 1.67e+04 1.22e+03 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[ 127.87 1385.62  300.    417.39  520.57]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.68\n",
      "F: 0.72\n",
      "variant: 4 (best/2/exp)\n",
      "[116704.73    220.21    300.      417.87    518.19]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[147435.23   1230.72    300.89    434.79    520.49]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1.26e+07 2.44e+09 1.49e+04 5.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[2.06e+07 1.38e+09 1.65e+04 5.07e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[5.06e+05 1.74e+04 3.35e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2514.31  200.    300.    408.74  520.42]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[109189.13    200.63    302.78    410.72    520.17]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[20799.56   200.01   300.     434.78   520.13]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.65\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[154881.53    834.95    300.99    426.14    520.45]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[3.10e+06 8.83e+08 6.54e+03 5.45e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1280.71 1004.14  300.    434.78  520.52]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[ 225.81 2124.41  300.    408.73  520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[795.87 200.   371.85 419.56 520.49]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.59\n",
      "F: 0.71\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[3781.57  200.    515.91  410.03  520.47]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.57\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[61122.21   209.82   348.3    411.98   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3004.03  200.    313.6   401.26  520.35]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.75e+06 9.95e+08 6.75e+03 4.57e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[8.46e+05 2.46e+05 1.89e+03 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2351.73  200.    300.    409.79  520.38]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.97\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.02 200.71 300.   417.39 520.55]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[54326.04   200.04   533.52   408.87   520.13]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 5 (rand/2/exp)\n",
      "[ 5257.55 13588.76   300.46   410.09   520.31]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[31813.02   221.91   377.69   436.04   520.13]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.92\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[121.23 212.07 300.   417.39 520.33]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 7 (rand/1/bin)\n",
      "[16625.88   979.86   300.     426.09   520.44]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.89\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1425.01  801.26  319.89  418.49  520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21487.06   200.     300.     426.09   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.55\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[94281.16   200.16   300.05   426.11   520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[81487.7    202.36   300.01   401.56   520.13]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.55\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[87362.04   227.2    300.07   419.56   517.57]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[3.68e+05 2.19e+06 3.97e+02 4.14e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[102.1  748.52 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[23944.     200.04   300.     402.17   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10279.66   200.04   300.02   418.47   517.66]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[7175.11  200.01  300.    418.48  520.51]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[13802.59   200.17   300.56   434.78   520.22]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.42e+06 3.66e+07 7.92e+02 4.46e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.59\n",
      "F: 0.45\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[29923.93  1666.64   300.01   419.74   520.4 ]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[2.75e+06 2.37e+08 1.40e+03 4.78e+02 5.21e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.89\n",
      "F: 0.72\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.64e+05 3.06e+07 9.50e+02 4.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.60\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[5.90e+05 6.91e+03 3.18e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[2397.69  200.01  300.    417.6   520.31]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[431.57 362.92 300.   426.09 520.33]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.55e+06 9.79e+07 6.15e+03 4.74e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.99\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.68e+05 2.54e+06 2.47e+03 4.43e+02 5.20e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.66\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[98002.33   343.47   300.69   401.23   520.42]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1.53e+07 3.17e+09 7.62e+03 7.21e+02 5.20e+02]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[281610.91   5124.55    312.95    410.03    520.45]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.96\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[87784.22   743.64   377.29   427.17   520.33]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.58\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[278692.65   2073.48    310.19    409.92    520.44]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   210.66 300.   427.17 520.54]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[24206.66   200.01   499.27   416.31   520.15]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.55\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[2.62e+06 3.59e+06 1.71e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 16\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.83e+06 5.77e+04 1.02e+03 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[70223.16  3742.49  1724.86   418.94   520.22]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.69\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[45909.41   394.3    300.06   418.49   519.99]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1487.5   200.    300.    418.47  519.93]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[14900.31   200.01   300.     408.7    520.16]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.57\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[196313.32    263.52    300.12    411.46    518.62]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[16290.36   327.2   1333.53   427.55   520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1888.87  200.   2570.51  418.52  520.39]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[100.02 200.   300.   426.09 520.42]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1010.21  200.    836.03  411.01  520.46]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.95\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1.04e+07 8.69e+08 8.87e+03 4.68e+02 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.61\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6.78e+05 2.91e+04 3.78e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8638.94  200.    300.    400.    520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[136437.59    200.78    300.01    434.78    520.32]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.97e+05 1.98e+08 1.45e+03 4.36e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 10 (rand/2/bin)\n",
      "[5.88e+05 3.22e+04 3.31e+02 4.27e+02 5.21e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8794.23  200.    300.    417.39  520.12]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5.53e+05 2.66e+02 3.01e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19443.67   200.     300.     418.56   520.17]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1728.4   200.01  300.    417.39  520.26]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1590.4   200.    300.46  409.92  520.39]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[11485.57   200.     305.69   409.78   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.41e+07 3.55e+09 1.37e+04 8.11e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[152710.01   4514.1     303.04    418.72    520.52]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[127.5  204.83 300.   426.09 520.37]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.99\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[6.18e+04 1.21e+07 3.84e+02 4.28e+02 5.16e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1606.39  200.25  300.    418.47  519.83]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1878.44   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.57\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1283.94  200.    312.22  418.58  520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[6172.61  200.    302.2   416.42  520.48]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[230906.27   1053.1     301.67    409.9     520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.21e+06 3.45e+07 6.32e+03 4.50e+02 5.20e+02]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.46\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[22640.29   200.     300.     426.09   520.41]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[4986.66  200.    300.    434.78  520.47]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.71\n",
      "F: 0.23\n",
      "variant: 9 (best/2/bin)\n",
      "[6.85e+07 4.99e+09 4.21e+04 6.85e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.89\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[586.06 423.96 300.01 408.7  520.28]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4.07e+05 1.80e+04 3.19e+02 4.10e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[1.04e+07 5.50e+09 1.74e+04 6.33e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[49790.07   203.57   303.64   419.6    520.14]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[174.61 221.05 300.   427.17 520.09]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.42\n",
      "variant: 4 (best/2/exp)\n",
      "[21308.19   203.99   608.67   409.83   520.14]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[624.52 716.1  300.   426.37 520.52]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[245.86 377.09 300.   426.09 520.32]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.58\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[59803.89   245.64   300.03   401.25   520.13]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[100.02 200.12 300.   417.39 520.27]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[2128.31  200.    359.    435.6   520.47]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[9387.25  200.01  300.    426.09  520.57]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[65264.18   200.01  1286.83   409.07   520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[7174.71  200.    311.88  418.63  520.42]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[112.12 211.96 300.   426.09 520.57]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.80e+06 5.49e+07 1.10e+03 4.75e+02 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[21414.55   200.     317.34   434.78   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.54\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[221106.02    335.65    301.33    410.12    520.14]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.95\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6475.03  211.8   300.71  418.67  519.57]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[29502.28   200.     300.     410.86   520.05]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[23360.17   200.     300.     434.78   520.14]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.42e+06 8.36e+07 3.98e+03 4.79e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.54\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[226822.23    402.38    300.06    404.01    520.12]\n",
      "\n",
      "pop_size: 44\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[59724.54  4922.95   319.17   441.26   520.44]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[10316.04   200.03   300.     426.09   520.12]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[17249.38   200.     359.64   418.47   520.18]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.00e+06 6.93e+08 1.34e+04 7.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.98\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[494.83 200.   300.   417.39 520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[20960.86   200.     300.03   409.81   520.42]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[684.25 200.01 300.   434.78 520.31]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[828.54 200.   300.   434.78 515.27]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 10 (rand/2/bin)\n",
      "[ 501.28 1124.4   300.    401.09  520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.89\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[768.01 200.   300.   434.78 520.29]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.83\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1.49e+07 1.31e+08 2.35e+03 4.42e+02 5.20e+02]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.99\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.36e+04 6.40e+06 3.43e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.68\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[81888.55   201.24   300.     402.28   520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.54\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[22507.39   200.68   300.02   426.14   520.4 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[17115.01   200.02   300.     418.47   520.13]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.56\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[153814.82    203.52    300.01    427.17    520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[1549.75  200.    368.23  426.1   520.49]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[21779.81   200.     675.83   434.78   520.13]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[438.89 400.72 300.   408.7  520.5 ]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2079.67 1108.92  439.98  418.66  520.44]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[367.3  288.05 300.   418.48 520.28]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[7094.13  200.    300.    409.78  520.48]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.75e+06 4.62e+06 1.86e+03 4.33e+02 5.18e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.54\n",
      "F: 0.67\n",
      "variant: 4 (best/2/exp)\n",
      "[1.82e+06 2.88e+05 8.95e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6.42e+06 2.64e+09 1.13e+04 5.75e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[5.93e+05 6.95e+05 2.15e+03 4.35e+02 5.18e+02]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.64\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[1.61e+06 4.93e+05 8.62e+02 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.89\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[80833.86  1717.67   984.42   419.11   520.22]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[9385.93  200.    440.02  409.03  520.4 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.66\n",
      "F: 0.38\n",
      "variant: 9 (best/2/bin)\n",
      "[4260.05  200.    300.    417.43  520.52]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55125.3    200.     300.     425.13   516.19]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.64\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[71044.51   200.01   300.     409.92   520.14]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[100.   271.91 300.   418.48 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[25181.4    200.     304.4    417.39   520.17]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[259.54 200.   300.   417.39 520.44]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[3.24e+05 3.07e+07 1.39e+03 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[120895.11   1191.91    301.4     417.46    520.53]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.73\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[50383.63   248.24   300.01   418.54   520.28]\n",
      "\n",
      "pop_size: 15\n",
      "CR: 0.88\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[1705.28  200.    300.    408.71  520.27]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[ 535.84 3609.88  300.    401.13  520.44]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[81487.7    202.36   300.01   401.43   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 5 (rand/2/exp)\n",
      "[17721.62   200.     300.     417.55   520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[1.16e+07 1.06e+09 5.74e+03 5.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[121461.64   3358.05    598.7     409.87    520.55]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.98\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[104.09 200.02 300.   418.47 520.47]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[43349.75   200.03   300.     400.1    520.15]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[4277.4   200.4   300.    427.17  520.41]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1236.63  200.    322.3   409.91  520.55]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[623.47 200.02 300.   408.7  520.34]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 10 (rand/2/bin)\n",
      "[4.98e+06 1.62e+08 2.95e+03 4.71e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1.06e+07 1.16e+09 2.61e+04 2.19e+03 5.20e+02]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1371.48  202.09  629.76  408.94  520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[36905.89   200.05   300.09   426.09   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[3.61e+07 4.17e+09 1.19e+04 1.18e+03 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.57\n",
      "F: 0.56\n",
      "variant: 4 (best/2/exp)\n",
      "[5.08e+05 6.16e+04 5.89e+02 4.23e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3.08e+06 7.04e+02 7.69e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[129526.34    650.25    300.7     426.14    520.2 ]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.55\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[6.37e+05 1.32e+04 5.67e+02 4.13e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.91\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[4389.34 1124.49  305.26  418.54  520.22]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[100.02 200.   300.   434.78 520.52]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1176.82 1288.5   300.75  434.78  520.59]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 1.00\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.27e+06 5.23e+06 2.56e+03 4.22e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.90\n",
      "F: 0.47\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[46550.63   694.8    373.74   419.38   520.32]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[74721.27   206.77   300.     418.52   520.15]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[17421.3    200.04   300.     434.78   520.43]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.57\n",
      "variant: 4 (best/2/exp)\n",
      "[5429.36  205.2   342.46  426.11  518.79]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 0.70\n",
      "F: 0.35\n",
      "variant: 4 (best/2/exp)\n",
      "[22674.82   215.58   300.01   417.47   520.18]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.99\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[2.26e+07 2.22e+09 2.26e+04 5.92e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[5.66e+05 1.58e+04 3.10e+02 4.11e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[1.81e+06 1.50e+08 7.90e+03 4.31e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[26400.49  1110.58  1083.07   426.61   520.13]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.66\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1163.88  200.    688.78  410.02  520.46]\n",
      "\n",
      "pop_size: 53\n",
      "CR: 0.94\n",
      "F: 0.41\n",
      "variant: 9 (best/2/bin)\n",
      "[1559.25  848.97  300.    426.6   520.5 ]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[12788.4    200.01   300.     417.54   520.18]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[74077.03   264.29   456.94   426.29   520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[6.77e+05 1.17e+06 3.75e+02 4.09e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[5.89e+05 1.83e+07 5.37e+03 4.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[5.63e+05 9.77e+03 3.27e+02 4.01e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.84\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5198.36  200.    300.    417.4   520.21]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[11510.89   215.26   300.01   426.09   520.23]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.55\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[4.85e+05 3.49e+03 3.68e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[57975.43  2920.68  1293.34   444.28   520.16]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[102.43 200.22 300.   418.47 520.5 ]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[1.12e+07 7.97e+08 4.69e+03 4.88e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.83\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[5029.29  200.    314.14  417.47  520.24]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[1.22e+04 1.55e+06 3.15e+02 4.22e+02 5.21e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.83\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2.52e+07 2.55e+09 1.55e+04 8.58e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.66\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.46e+06 8.86e+05 1.13e+03 4.18e+02 5.20e+02]\n",
      "\n",
      "pop_size: 14\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5.81e+05 5.95e+02 3.11e+02 4.02e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[6.23e+05 5.72e+05 9.82e+02 4.41e+02 5.21e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.06 200.01 300.   417.39 520.47]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[35897.96   331.61   341.71   409.88   520.48]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8956.35  200.    300.    410.89  520.16]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.71\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[30728.83   725.06   300.26   426.1    520.5 ]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.97\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1.25e+06 5.99e+08 5.89e+03 4.99e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.56\n",
      "F: 0.70\n",
      "variant: 9 (best/2/bin)\n",
      "[2.31e+06 2.60e+06 1.52e+03 4.29e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.66\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[31317.65   407.93  1874.42   433.69   520.15]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.88\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1728.4   200.01  300.    417.39  520.26]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[876.63 200.   300.   408.82 520.56]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[45269.58   200.01   300.     427.17   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.57\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49234.78   200.     300.03   434.78   520.42]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[102554.49    218.65    300.01    417.6     520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 5 (rand/2/exp)\n",
      "[6.49e+07 2.26e+09 1.07e+04 2.65e+03 5.21e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4.70e+05 6.44e+03 3.12e+02 4.19e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[174.61 221.05 300.   427.17 520.09]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.98\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[342.86 209.92 300.   427.17 520.38]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[18837.83   200.     300.     402.17   520.14]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.55\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[5.58e+05 5.24e+03 3.89e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 6\n",
      "CR: 0.66\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[3137.23  200.37  368.33  410.86  520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.75\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[21794.87   200.     303.39   410.91   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[60300.88   741.22   321.43   434.78   520.45]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4459.9   200.    304.12  408.88  520.41]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[6773.94  200.03  300.    427.17  520.54]\n",
      "\n",
      "pop_size: 11\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[4962.24  961.37  747.34  409.92  520.22]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[1.60e+06 3.00e+08 4.56e+03 4.97e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[164215.54   1524.31    302.03    434.79    520.52]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[64432.78   201.57   300.     409.84   520.14]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.56\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[10815.27   200.     301.52   418.48   520.37]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[68241.68  1636.11   303.06   418.51   519.83]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[2.70e+07 3.72e+09 1.97e+04 5.65e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[3.77e+05 1.09e+03 3.31e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.52\n",
      "variant: 10 (rand/2/bin)\n",
      "[40826.92   200.     301.69   426.1    520.38]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 9 (best/2/bin)\n",
      "[624.52 716.1  300.   426.37 520.52]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[16825.51   200.     300.     409.78   520.14]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.97\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[316.67 200.   300.   426.09 520.38]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.57\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[196313.32    263.52    300.12    411.46    518.62]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[15929.21   200.     874.76   402.27   516.97]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1487.38  200.    300.    427.17  520.43]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10672.3    200.    1044.07   417.39   520.13]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[6.07e+05 3.85e+04 3.48e+02 4.22e+02 5.20e+02]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[2.93e+07 2.40e+09 1.01e+04 6.99e+02 5.21e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[29623.41  4045.64   949.17   410.08   520.42]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.59\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[4.92e+05 2.07e+06 1.44e+03 4.55e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10655.55   200.     300.     400.12   520.11]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.38\n",
      "variant: 4 (best/2/exp)\n",
      "[8.14e+05 1.45e+08 4.39e+03 4.30e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[6273.7   200.17  300.    418.47  520.36]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5478.69 2293.52  300.    435.05  520.5 ]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4876.96  200.    300.    400.    520.21]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[32165.62  4214.2    312.65   426.09   520.43]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[1330.26  847.51  300.    418.68  520.46]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.58\n",
      "variant: 9 (best/2/bin)\n",
      "[828.45 822.2  300.01 427.18 520.55]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.35\n",
      "variant: 9 (best/2/bin)\n",
      "[1.46e+07 2.68e+08 4.68e+03 6.25e+02 5.21e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[8638.94  200.    300.    401.09  520.17]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[21779.81   200.     675.83   434.78   520.13]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6289.73  201.08  310.72  426.09  520.13]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[1606.39  200.25  300.    418.47  519.83]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.71\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[95725.29   205.42  2111.99   427.4    520.48]\n",
      "\n",
      "pop_size: 52\n",
      "CR: 0.90\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[691.34 202.14 300.01 426.09 520.63]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 9 (best/2/bin)\n",
      "[ 435.93 1633.45  305.65  418.81  515.34]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[1861.97  200.01  300.    408.7   520.32]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[65386.5    526.88   300.96   410.95   520.55]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[3549.25 1911.24  794.51  409.96  520.23]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1888.87  200.   2570.51  418.52  520.39]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[6009.63  200.    300.    426.09  510.34]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.99\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[462.8  200.   300.   401.08 520.49]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.53\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.66e+05 4.26e+02 3.11e+02 4.03e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.69\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[119230.15    669.08    300.2     418.54    520.23]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.86\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[13469.48   200.     307.78   418.5    520.22]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.55\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[5.57e+05 7.73e+02 3.06e+02 4.04e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[1675.77  206.35  318.4   427.17  520.46]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[4789.22  200.    300.    434.78  520.42]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2140.02  200.    300.    426.09  520.5 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[2.25e+06 8.42e+07 7.14e+03 4.40e+02 5.20e+02]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.65\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[150429.1     835.53    301.      426.16    520.38]\n",
      "\n",
      "pop_size: 39\n",
      "CR: 0.67\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.44e+05 2.67e+03 3.02e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2252.88  200.    300.    417.41  520.52]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[13019.86   200.04  1401.71   417.51   520.15]\n",
      "\n",
      "pop_size: 58\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[3545.39  985.35  315.4   419.95  520.52]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.58\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[10869.57   200.     300.     402.21   520.16]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[56476.75   200.01   300.     408.93   520.15]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.68\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[4.83e+05 2.99e+04 3.40e+02 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.97\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[8.65e+06 2.24e+08 5.86e+03 4.61e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5.88e+04 2.20e+07 1.59e+03 4.50e+02 5.16e+02]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[56849.04   235.08   300.06   434.78   520.51]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[6013.49  202.76  300.    411.95  518.38]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.70\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[25181.4    200.     304.4    417.39   520.17]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 1.00\n",
      "F: 0.53\n",
      "variant: 3 (rand-to-best/1/exp)\n",
      "[7.70e+05 6.36e+03 2.61e+03 4.44e+02 5.21e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.58\n",
      "F: 0.51\n",
      "variant: 9 (best/2/bin)\n",
      "[1070.99  200.    320.18  410.99  520.44]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[10010.29   200.     306.72   400.34   520.15]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[851.55 200.   300.   426.09 520.26]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[428.23 291.7  300.   419.56 520.37]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[780.8  200.03 300.   426.09 519.96]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 9 (best/2/bin)\n",
      "[795.87 200.   371.85 419.56 520.49]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 8 (rand-to-best/1/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.79e+06 9.46e+06 9.46e+02 4.63e+02 5.20e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[140.99 200.01 300.   408.86 520.52]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[19443.67   200.     300.     418.56   520.17]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.72\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[19124.     200.     300.     409.8    520.14]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[30500.63  1852.58   300.21   410.01   520.56]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.68\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[2904.15  200.01  300.    409.79  520.39]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[463.63 982.83 300.   411.83 520.49]\n",
      "\n",
      "pop_size: 12\n",
      "CR: 0.90\n",
      "F: 0.70\n",
      "variant: 4 (best/2/exp)\n",
      "[2939.91  200.04  300.    419.56  520.25]\n",
      "\n",
      "pop_size: 46\n",
      "CR: 0.55\n",
      "F: 0.37\n",
      "variant: 4 (best/2/exp)\n",
      "[263819.12   3136.23    318.58    417.69    520.22]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[12464.08   200.     300.     410.86   520.17]\n",
      "\n",
      "pop_size: 48\n",
      "CR: 1.00\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[1617.68 5181.29  300.    402.61  520.45]\n",
      "\n",
      "pop_size: 5\n",
      "CR: 0.91\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[8.08e+07 4.87e+09 2.95e+04 1.01e+03 5.20e+02]\n",
      "\n",
      "pop_size: 66\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[7.27e+05 9.46e+04 3.89e+02 4.12e+02 5.20e+02]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[7758.93  200.11  300.    402.17  518.54]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[1280.71 1004.14  300.    434.78  520.52]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.99\n",
      "F: 0.52\n",
      "variant: 9 (best/2/bin)\n",
      "[1.39e+07 2.00e+09 1.80e+04 5.89e+02 5.21e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.69\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[6.03e+05 5.37e+04 3.38e+02 4.20e+02 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.55\n",
      "F: 0.69\n",
      "variant: 4 (best/2/exp)\n",
      "[1.13e+06 6.00e+05 1.08e+03 4.16e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[2.41e+07 3.76e+09 1.40e+04 8.08e+02 5.21e+02]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.52\n",
      "F: 0.69\n",
      "variant: 9 (best/2/bin)\n",
      "[2.68e+06 2.83e+06 1.67e+03 4.27e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.94\n",
      "F: 0.40\n",
      "variant: 4 (best/2/exp)\n",
      "[1.78e+07 2.10e+09 1.49e+04 9.48e+02 5.20e+02]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.70\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5310.01  200.    300.    434.78  520.54]\n",
      "\n",
      "pop_size: 9\n",
      "CR: 0.91\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[55964.57  2512.14  2064.22   420.54   520.22]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.91\n",
      "F: 0.40\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[4.18e+08 8.51e+09 1.60e+04 1.03e+03 5.20e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.70\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[5.22e+05 3.52e+04 3.24e+02 4.28e+02 5.20e+02]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.83\n",
      "F: 0.68\n",
      "variant: 9 (best/2/bin)\n",
      "[1171.6   200.    300.56  410.86  520.49]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.67\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[106313.47    667.98    300.58    426.11    520.23]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[100.   524.2  300.08 409.78 520.46]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.65\n",
      "F: 0.53\n",
      "variant: 4 (best/2/exp)\n",
      "[9.27e+05 6.41e+04 3.95e+02 4.25e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.92\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n",
      "[9.56e+06 1.67e+07 4.08e+03 4.34e+02 5.20e+02]\n",
      "\n",
      "pop_size: 51\n",
      "CR: 1.00\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[4.01e+04 3.73e+06 3.17e+02 4.33e+02 5.20e+02]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.81\n",
      "F: 0.50\n",
      "variant: 4 (best/2/exp)\n",
      "[58571.98  3319.12   567.52   419.08   520.2 ]\n",
      "\n",
      "pop_size: 49\n",
      "CR: 0.58\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[19762.11   200.05   300.     426.13   520.37]\n",
      "\n",
      "pop_size: 8\n",
      "CR: 0.67\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[31989.96   691.78   361.44   411.04   520.43]\n",
      "\n",
      "pop_size: 47\n",
      "CR: 0.69\n",
      "F: 0.52\n",
      "variant: 4 (best/2/exp)\n",
      "[3.30e+05 1.12e+04 3.06e+02 4.01e+02 5.20e+02]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[4.74e+06 2.28e+08 5.03e+03 4.62e+02 5.16e+02]\n",
      "\n",
      "pop_size: 54\n",
      "CR: 0.90\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[19189.21   200.01   300.     417.53   520.26]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.91\n",
      "F: 0.41\n",
      "variant: 4 (best/2/exp)\n",
      "[16284.89   200.01   300.     434.78   520.37]\n",
      "\n",
      "pop_size: 55\n",
      "CR: 0.65\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[35432.44   200.04   300.     434.78   520.48]\n",
      "\n",
      "pop_size: 50\n",
      "CR: 0.92\n",
      "F: 0.39\n",
      "variant: 9 (best/2/bin)\n",
      "[5584.58 3192.71  307.18  435.06  520.59]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.70\n",
      "F: 0.68\n",
      "variant: 4 (best/2/exp)\n",
      "[29916.1    200.     300.     402.21   517.39]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.55\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[102554.49    218.65    300.01    416.52    520.17]\n",
      "\n",
      "pop_size: 10\n",
      "CR: 0.70\n",
      "F: 0.54\n",
      "variant: 4 (best/2/exp)\n",
      "[7089.77  200.    300.    418.48  520.16]\n",
      "\n",
      "pop_size: 13\n",
      "CR: 0.68\n",
      "F: 0.54\n",
      "variant: 8 (rand-to-best/1/bin)\n",
      "[5.01e+06 1.35e+08 3.54e+03 4.47e+02 5.21e+02]\n",
      "\n",
      "pop_size: 56\n",
      "CR: 0.66\n",
      "F: 0.39\n",
      "variant: 4 (best/2/exp)\n",
      "[119580.58   2258.3     304.23    426.27    520.18]\n",
      "\n",
      "pop_size: 7\n",
      "CR: 0.91\n",
      "F: 0.53\n",
      "variant: 9 (best/2/bin)\n",
      "[6.37e+06 4.90e+08 1.14e+04 5.15e+02 5.21e+02]\n",
      "\n",
      "CPU times: user 4min 54s, sys: 8min 8s, total: 13min 3s\n",
      "Wall time: 49min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best_meta_fitness, best_parameters = \\\n",
    "    meta_optimize(pop_size=40, num_generations=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Parameters Found\n",
    "\n",
    "Let us first show the best meta-fitness values we found during meta-optimization. This shows the trade-off between the different choices of DE parameters on the 5 benchmark problems. Note how the first column is sorted ascendingly. Other columns may also appear to be sorted ascendingly, which means the same choices of parameters perform well on those different problems. Other columns may appear to be unsorted or perhaps sorted descendingly, which means the parameters perform well on some but not all the problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.00e+02 5.12e+02 3.00e+02 4.10e+02 5.20e+02]\n",
      " [1.02e+02 7.49e+02 3.00e+02 4.26e+02 5.21e+02]\n",
      " [1.75e+02 2.21e+02 3.00e+02 4.27e+02 5.20e+02]\n",
      " [2.26e+02 1.89e+03 3.00e+02 4.09e+02 5.21e+02]\n",
      " [4.28e+02 2.92e+02 3.00e+02 4.20e+02 5.20e+02]\n",
      " [4.36e+02 1.63e+03 3.06e+02 4.19e+02 5.15e+02]\n",
      " [4.63e+02 2.00e+02 3.92e+03 4.19e+02 5.20e+02]\n",
      " [4.63e+02 2.00e+02 3.92e+03 4.19e+02 5.20e+02]\n",
      " [4.64e+02 9.83e+02 3.00e+02 4.12e+02 5.20e+02]\n",
      " [5.36e+02 3.61e+03 3.00e+02 4.01e+02 5.20e+02]\n",
      " [5.36e+02 3.61e+03 3.00e+02 4.01e+02 5.20e+02]\n",
      " [7.96e+02 2.00e+02 3.72e+02 4.20e+02 5.20e+02]\n",
      " [8.01e+02 2.09e+03 3.05e+02 4.35e+02 5.16e+02]\n",
      " [8.29e+02 2.00e+02 3.00e+02 4.35e+02 5.15e+02]\n",
      " [1.61e+03 2.00e+02 3.00e+02 4.18e+02 5.20e+02]\n",
      " [1.73e+03 2.00e+02 3.00e+02 4.17e+02 5.20e+02]\n",
      " [1.86e+03 2.00e+02 3.00e+02 4.09e+02 5.20e+02]\n",
      " [1.89e+03 2.00e+02 2.57e+03 4.19e+02 5.20e+02]\n",
      " [1.89e+03 2.00e+02 2.57e+03 4.19e+02 5.20e+02]\n",
      " [3.55e+03 1.91e+03 7.95e+02 4.10e+02 5.20e+02]\n",
      " [4.88e+03 2.00e+02 3.00e+02 4.00e+02 5.20e+02]\n",
      " [6.01e+03 2.00e+02 3.00e+02 4.26e+02 5.10e+02]\n",
      " [6.01e+03 2.00e+02 3.00e+02 4.26e+02 5.10e+02]\n",
      " [6.01e+03 2.03e+02 3.00e+02 4.12e+02 5.18e+02]\n",
      " [6.27e+03 2.00e+02 3.00e+02 4.18e+02 5.20e+02]\n",
      " [7.76e+03 2.00e+02 3.00e+02 4.02e+02 5.19e+02]\n",
      " [1.00e+04 2.00e+02 3.07e+02 4.00e+02 5.20e+02]\n",
      " [1.07e+04 2.00e+02 1.04e+03 4.17e+02 5.20e+02]\n",
      " [1.47e+04 2.00e+02 3.00e+02 4.04e+02 5.20e+02]\n",
      " [1.59e+04 2.00e+02 8.75e+02 4.02e+02 5.17e+02]\n",
      " [1.59e+04 2.00e+02 8.75e+02 4.02e+02 5.17e+02]\n",
      " [2.14e+04 2.00e+02 3.17e+02 4.33e+02 5.20e+02]\n",
      " [2.18e+04 2.00e+02 6.76e+02 4.35e+02 5.20e+02]\n",
      " [2.99e+04 2.00e+02 3.00e+02 4.02e+02 5.17e+02]\n",
      " [5.51e+04 2.00e+02 3.00e+02 4.22e+02 5.16e+02]\n",
      " [5.51e+04 2.00e+02 3.00e+02 4.25e+02 5.16e+02]\n",
      " [3.97e+05 4.80e+02 3.01e+02 4.02e+02 5.20e+02]\n",
      " [3.97e+05 4.80e+02 3.01e+02 4.02e+02 5.20e+02]\n",
      " [4.45e+05 3.12e+02 3.00e+02 4.02e+02 5.20e+02]\n",
      " [4.45e+05 3.12e+02 3.00e+02 4.02e+02 5.20e+02]]\n"
     ]
    }
   ],
   "source": [
    "print(np.array2string(best_meta_fitness, precision=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print the corresponding parameters for the DE optimizer. These parameters are also sorted according to their performance on the first problem. The parameters may change each time you run the meta-optimization, but they probably show certain trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(51, 1.0, 0.52, 4),\n",
       " (54, 1.0, 0.52, 4),\n",
       " (54, 0.91, 0.51, 4),\n",
       " (50, 1.0, 0.52, 9),\n",
       " (54, 0.91, 0.52, 4),\n",
       " (49, 0.91, 0.4, 9),\n",
       " (10, 0.7, 0.53, 9),\n",
       " (10, 0.7, 0.53, 9),\n",
       " (56, 0.91, 0.39, 9),\n",
       " (50, 1.0, 0.5, 4),\n",
       " (50, 1.0, 0.5, 4),\n",
       " (10, 0.7, 0.54, 9),\n",
       " (55, 0.91, 0.39, 9),\n",
       " (54, 0.91, 0.41, 4),\n",
       " (55, 0.91, 0.39, 4),\n",
       " (54, 0.88, 0.41, 4),\n",
       " (51, 0.91, 0.41, 4),\n",
       " (8, 0.58, 0.53, 9),\n",
       " (8, 0.58, 0.53, 9),\n",
       " (9, 0.91, 0.53, 4),\n",
       " (8, 0.83, 0.68, 4),\n",
       " (49, 0.67, 0.39, 9),\n",
       " (49, 0.67, 0.39, 9),\n",
       " (8, 0.83, 0.7, 4),\n",
       " (49, 0.9, 0.39, 4),\n",
       " (13, 0.68, 0.54, 4),\n",
       " (7, 0.67, 0.53, 4),\n",
       " (8, 0.7, 0.53, 4),\n",
       " (9, 0.66, 0.52, 4),\n",
       " (8, 0.7, 0.52, 4),\n",
       " (8, 0.7, 0.52, 4),\n",
       " (9, 0.7, 0.52, 4),\n",
       " (7, 0.7, 0.53, 4),\n",
       " (7, 0.7, 0.68, 4),\n",
       " (8, 0.69, 0.52, 4),\n",
       " (8, 0.69, 0.52, 4),\n",
       " (10, 0.55, 0.69, 4),\n",
       " (10, 0.55, 0.69, 4),\n",
       " (8, 0.55, 0.68, 4),\n",
       " (8, 0.55, 0.68, 4)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[unpack_parameters(params) for params in best_parameters]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use this helper-function to get the parameters that performed best on a given problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_best_parameters(problem_idx):\n",
    "    \"\"\"\n",
    "    Get the best parameters for the given problem.\n",
    "    \n",
    "    Note that problem_idx is an integer between 0 and num_problems,\n",
    "    and is not the same as problem_ids above.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Find index of the best parameters for the given problem.\n",
    "    param_idx = np.argmin(best_meta_fitness[:, problem_idx])\n",
    "\n",
    "    # Unpack those parameters.\n",
    "    params = unpack_parameters(best_parameters[param_idx])\n",
    "    \n",
    "    return params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pareto Front\n",
    "\n",
    "The so-called Pareto front shows the solutions that optimally compromise between two conflicting objectives. When there are 3 or more objectives, the normal Pareto plot is not useful and has therefore been omitted here. You can copy the source-code from the [second paper](https://github.com/Hvass-Labs/MetaOps/blob/master/02_Multi-Objective_Meta-Optimization.ipynb) and try it here if you like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Performance\n",
    "\n",
    "We will now compare the performance of the DE parameters that we found above on each of the benchmark problems.\n",
    "\n",
    "The current version of Pygmo has very poor support for logging fitness traces while doing optimization, so this implementation is very awkward and needs several helper-functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_fitness_traces(params, problem, num_runs):\n",
    "    \"\"\"\n",
    "    Perform a number of optimization runs on the given problem\n",
    "    with DE using the given parameters and return a trace of\n",
    "    the mean fitness, and the corresponding count of fitness\n",
    "    evaluations.\n",
    "    \n",
    "    :param params: Array with DE parameters.\n",
    "    :param problem: Problem to optimize.\n",
    "    :param num_runs: Number of optimization runs.\n",
    "    :return: (trace_mean: 1-d numpy array,\n",
    "              steps: 1-d numpy array)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Unpack the DE parameters.\n",
    "    pop_size, CR, F, variant = \\\n",
    "        unpack_parameters(params=params)\n",
    "    \n",
    "    # The number of generations to evaluate.\n",
    "    num_generations = num_fitness_evals // pop_size\n",
    "    \n",
    "    def _single_fitness_trace():\n",
    "        \"\"\"Helper-function for doing a single optimization run.\"\"\"\n",
    "\n",
    "        # Create a new instance of the optimizer using\n",
    "        # the given parameters. ftol and xtol are set to zero\n",
    "        # so it does not abort the optimization upon convergence.\n",
    "        # This is necessary so we get the same log-size for all runs.\n",
    "        uda = pg.de(gen=num_generations,\n",
    "                    CR=CR, F=F, variant=variant,\n",
    "                    ftol=0.0, xtol=0.0)\n",
    "        optimizer = pg.algorithm(uda=uda)\n",
    "\n",
    "        # Set the optimizer to log after each GENERATION!\n",
    "        # This means we perform pop_size fitness evaluations\n",
    "        # between each entry in the log, which is why we\n",
    "        # cannot compare the logs directly for different parameters.\n",
    "        optimizer.set_verbosity(level=1)\n",
    "\n",
    "        # Create a population of agents for the optimizer.\n",
    "        population = pg.population(prob=problem, size=pop_size)\n",
    "\n",
    "        # Evolve the optimizer's population.\n",
    "        population = optimizer.evolve(population)\n",
    "\n",
    "        # Get the fitness-log from the optimizer using\n",
    "        # some extremely weird API calls in Pygmo.\n",
    "        uda = optimizer.extract(pg.de)\n",
    "        log = np.array(uda.get_log())\n",
    "        fitness_trace = log[:, 2]\n",
    "\n",
    "        # Return the fitness trace for this run.\n",
    "        return fitness_trace \n",
    "\n",
    "    # Perform the given number of optimization runs and\n",
    "    # get their fitness traces as a 2-dim array.\n",
    "    fitness_traces = [_single_fitness_trace()\n",
    "                      for i in range(num_runs)]\n",
    "    \n",
    "    # Convert to a numpy array so it is easier to work with.\n",
    "    fitness_traces = np.array(fitness_traces)\n",
    "    \n",
    "    # Take the average fitness-trace for all optimization runs.\n",
    "    trace_mean = fitness_traces.mean(axis=0)\n",
    "\n",
    "    # Calculate the number of steps (or fitness evaluations)\n",
    "    # corresponding to the entries in the trace. This is necessary\n",
    "    # to compare the fitness traces for different pop_size,\n",
    "    # because the fitness trace is logged after each GENERATION\n",
    "    # of the entire population, rather than after each\n",
    "    # fitness evaluation.\n",
    "    steps = np.arange(len(trace_mean)) * pop_size\n",
    "\n",
    "    return trace_mean, steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fitness_traces(problem_idx, num_runs=50):\n",
    "    \"\"\"\n",
    "    Perform the given number of optimization runs on the\n",
    "    given problem with different choices of parameters for\n",
    "    the DE optimizer and plot their fitness-traces for comparison.\n",
    "\n",
    "    Note that problem_idx is an integer between 0 and num_problems,\n",
    "    and is not the same as problem_ids above.\n",
    "    \"\"\"\n",
    "\n",
    "    # Create a single plot.\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(211)\n",
    "\n",
    "    # Get the problem and its name.\n",
    "    problem = problems[problem_idx]\n",
    "    problem_name = problem.get_name()\n",
    "    \n",
    "    for i in range(num_problems):\n",
    "        # Get the best parameters for the i'th problem.\n",
    "        params = get_best_parameters(problem_idx=i)\n",
    "        \n",
    "        # Fitness-trace for parameters tuned for the i'th problem.\n",
    "        trace, steps = \\\n",
    "            multi_fitness_traces(params=params,\n",
    "                                 problem=problem,\n",
    "                                 num_runs=num_runs)\n",
    "\n",
    "        # Plot the fitness traces.\n",
    "        label = \"DE Parameters Tuned for \" + problems[i].get_name()\n",
    "        ax.plot(steps, trace, label=label)\n",
    "    \n",
    "    # Set y-axis to log-scale.\n",
    "    ax.set_yscale(\"log\")\n",
    "    \n",
    "    # Set plot title and labels.\n",
    "    ax.set_title(\"Comparison of DE Parameters on: \" + problem_name)\n",
    "    ax.set_xlabel(\"Num. Fitness Evaluations\")\n",
    "    ax.set_ylabel(\"Mean Fitness (Log-Scale)\")\n",
    "    \n",
    "    # Show legend.\n",
    "    ax.legend()\n",
    "    \n",
    "    # Show grid.\n",
    "    ax.grid()\n",
    "    \n",
    "    # Plot with a compact layout.\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now show a plot for each of the problems we used during meta-optimization. In each plot we show 5 fitness-traces which show the average performance of 50 optimization runs using the parameters that performed best on each of the problems.\n",
    "\n",
    "Because of the stochastic nature of the DE optimizer, these plots may vary somewhat every time you run this, even if you don't change the DE parameters.\n",
    "\n",
    "Some of the fitness-traces may be highly correlated, so that parameters tuned for different problems seem to perform well on all those problems. Other fitness-traces may show opposite correlation, so the parameters only perform well on the problem for which they were tuned and perform poorly on the other problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAF9CAYAAAAZRJ4tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsnXlcVdXa+L8LNERwKAULKUktBeQAAinXkVTUHLOc0gRNTe81b3XfyuvLdUr7OaVe016vklOaQ+XVnG5J4iyJykEFL0qKiSiiIaCBCKzfH/uwO8wHFBFb38/nfM7Ze6/hWcPe59nPetZaQkqJQqFQKBQKhUKh0LCqagEUCoVCoVAoFIpHCaUgKxQKhUKhUCgUZigFWaFQKBQKhUKhMEMpyAqFQqFQKBQKhRlKQVYoFAqFQqFQKMxQCrJCoVAoFAqFQmGGUpAViscEIcQwIcQPVS1HPkIIWyHEdiFEmhDi66qWR6F4mAghWgghjEKIDCHERAvjbBBC9LcwrBRCNDf9Xi2EmGn63UEIEVdxycvMt9zlqmA+x4QQ7pWVvkJRFkpBVigKIYR4QwhxXAhxWwhxVQixWwjRvqrlKgsp5XopZWBVy2HG60AjoIGUcmDhi0KIaUKIe6Y/2gwhxDkhxBIhxDNmYToLIfJMbWH+8S8uQyFEghAi0xQm2aQ42FdeER8M5gpOdUcI8aIQ4mshxA3Ty9EpIcT7QghrIYSLSbEr3J6DzeK/JITYJYS4JYT41aQojTRdayuE2GM6n2LKx7y/CCHEHCHETdNnjhBCmF1fLoSIM/Wp4FLK8KNJzhr3URUfAuFSyjpSysVCiAAhRLipThKKydMAeALb7iNPpJQHpZQt7ieNMihQrkrMZz4woxLTVyhKRSnICoUZQoj3gUXAJ2jK3XPA50C/qpSrLO7zj7yyaAKck1LmlBJmk5SyDvAU8CrwNHDCXOkBkqSU9oU+R0tJs4+U0h5oDfgCIeUV/BGtzxJ5VOQVQjQDfgIuAx5SynrAQLR2qGMWtH6h9txkiu8P7AX2A82BBsB4oKcp3pPAcsAFrX9lAKvM0h0L9EdTNA1AH+Bts+vRwJ+Bk6WUYRhQs7xlL4YmQIzZ8R1gJfBBCeHfBtbLR3/3rsLlqiy+AwKEEE8/hLwUiqJIKdVHfdRHSoB6wG1gYClhbNAU6CTTZxFgY7rWGUhEs7BcB66i/Vm/ApwDfgUmm6U1DfgG2IT2R38S8DS7Pgn42XQtFnjV7FowcBhYCNwEZprOHTJdF6Zr14F04DTQyqyca4EU4BKaAmlllu4hNOtNKnAR6FlKfbgC+4BbaH+afU3npwPZwD1Tnb5VTNxpwLpC56zRlJj55nVajjZMALqaHc8Ddph+jwTOmurzAvC2Wbj8tvsIuAZ8iaaM7TDVU6rpt7NZnH2mej9iKuN2NIVuvanOIwEXs/AtgT2mfhAHDDKdH2uqp+z8dEznnYBvTflfBCYW03fWmfIaDbwEHDcdJwMLSqmnMUC8SZbvACezaxIYB5w3tetSQFhY/+uAnaVcdzGlX6OE64eApeVo79ZAhtnxEWCs2fFbQEQJ+QSX8Aw4B7QtTU4L5NoL5AJZpjZ90exaVyChmDgXgPaFzo0y9dlU4HugSaF2am76vRqYWdw9g3ZP/B3tGZKK9kJRy3Stoalf3zL1hYOYngWWlgvtPhhtFiYY03PIkv5k6ov592Us0Nrs2h4gqCJtoD7qc78fZUFWKH7HH6gF/LuUMP+L9ufphWaleomCFsqnTWk0BqYAK4DhgA/QAfiHEOJ5s/D9gK/RLKhfAVuFEPnWq59NceqhKZzrCllW26D9qTYCZhWSMxDoiPYHVg8YhKZIA3xmOtcU6ASMQFMezdONQ/vznAt8YT5MnY9Jzu3AD4Aj8A6wXgjRQko5Fc0Kv0lqFsIvCscvDillLtoQcwdLwpeGEOJZtJeTKNOp60BvoC5aeRcKIVqbRXkarR2aoCmtVmjKRBO0kYRMYEmhbIYAb6K1dzPgqCnOU2h/+lNNstih/dl/hVZXQ4DPhRBuUsrlaEr1XFNd9RFCWKHVbbQp7S7Au0KI7mZ590NTkuub4v8T+KeUsq5Jls0l1MvLwP9D6xPPoL0kbSwUrDfgh2aFHQR0N8V9zuT68FxxaaMpf9+UcK1UhBC10e7B8sTvSEFrpjtaneUTbTpnKZ8A/4f2klRhpJQvoymbE0xteq608Kb+8TzafZd/rh8wGRgAOJjS21BBkYahtWEztGdC/jPrb2gvhg5oz5HJaAptsZS3XGaU1J8Gor3sjUC7L/vy+3MKtHvI08I8FIoHilKQFYrfaQDckKW7BAwDZkgpr0spU9AU1zfNrt8DZkkp76EpHQ3RlJYMKWUMmoXE/IF/Qkr5jSn8AjTlui2AlPJrKWWSlDJPakPQ59EU8nySpJSfSSlzpJSZheS8hzak3RLNWnNWSnlVCGGNppz93SRTAvBpoTJcklKuMCmra9CUqEbF1EVbwB6YLaXMllLuRbNGDS2l/iwhCU3BzMfJpJSZf+xKib9VCHELzUq4H03pQUq5U0r5s9TYj6bYmyviecBUKeVdKWWmlPKmlPJbKeVvUsoMtJeQToXyWmVKMw3YDfwspQwz9aGvAW9TuN5oVsNVpvaKQrMOF/HNNuEHOEgpZ5jq9gLay9YQszBHpZRbTf0jE63NmwshGkopb0spI0pIexiwUkp5Ukp5F8266C+EcDELM1tKeUtK+QsQjvZCiJTyFyllfdP54miANnJSFjcKtacrmsXeysL4+T67UyjosmAPpJkdpwH2xb3gFZOeL9AO7QXyYVPf9J1hdm4c8P9M924OWj/2EkI0qUD6S6SUl6WUv6L14/x79B7a/d1ESnlPav7LleHiUWx/Qhv5mCuljDTdl/FSyktm8TL4vW4UioeKUpAVit+5CTQsw5/TCc3ils8l0zk9DZNiCZrFEbThbszOmU8au5z/Q0qZh2bNcQIQQowQ2mzxWyaFrxWawl0kbmFMyuoStOHM66bJSXVN8WsWU4bGZsfXzNL5zfSzuIluTsBlk9wlpVURGqMN9+aTZFLKzD93Sonf3xSmiZTyz/kvD0KInkKICNMEr1to1mXz+kyRUmblHwghagsh/iWEuCSESAcOAPVNLxn5FG7bktq6CdDGXClEU1RL8q9sQqEXAzTrnvmLSuH2fwvNOvhfIUSkEKJ3CWkX6MNSyttofb/YPgD8RvHtXxw30RSusmhYqD3z3QjyLIkvtNUbdgN/lVIeNLt0G80SmU9d4HZZSp/JYv+5Kb3SXpDzwy8zm2A4uazwFnDL9G3up90E+KdZ+/+K5jpVkfvLvK+YP7Pmobna/CCEuCCEmFSBtC2hpP70LNpIWUnU4fe6USgeKkpBVih+5yhwF81vuCSS0P648nnOdK6iPJv/w/Qn7QwkmaxEK4AJaKtA1AfOoP1B5lPqn76UcrGU0gdwQ1OcPgBuoFmNCpfhSgVkTwKeNcl9v2kBeh30QRvGfWAIIWzQLLbzgUam+txF6fX5N6AF0MbkttAxP7kKiHAZ2F9IKbSXUo4vIe/LwMVC4etIKV8pSV4p5Xkp5VA0F445wDclWNoL9GFTmAbcR7uZEQa8VpGIppexo2XFN90bYcDHUsovC12OoeAIjSeWTSirizaRcJMQ4hqa/zhAohCiiLuPlHKc/H2C4ScWpF8qphe+n9Hu03wuo/nJm/cBWynlkQpk8azZb/2ZZRpF+puUsimae8P7Qogu5Uz7DlDb7Lg8k+ouo7l9lIQrBV1mFIqHhlKQFQoTpmHyKcBSIUR/kwWxpsnyONcUbAMQIoRwEEI0NIVfdx/Z+gghBpis1u+iKegRgB2aApQCILRlrlpZmqgQwk8I0cbkJ3wHbVJNnsm6vRmYJYSoY1I23q9gGX5CswZ9aKqnzmjKbWF/VkvkrWEaZt+A9ge7oALylMYTaBMsU4AcIURPND/t0qiDZgW+JYR4CpM/cQXZAbwohHjTVFc1TW3karqejOYTns8xIEMI8ZHQ1pO2FkK0EkL4lZSBEGK4EMLBZNHPt7rlFRN0AzBSCOFlenH4BPjJ5G5zv0wF/iSEmJe/+oAQorkQYp0QwpKh8g+BYCHEB0KIBqb4nkKIjabfjdEmii2RUi4rJv5aNCWvsRDCCe0lZ3X+RSHEE0KIWmgvOTWFELVML2VpaFZVL9Mn/0XEB62f3zdCCCtT3jW1Q1FLCPGEWZBdFHThWQb8XZjWAhZC1DP57FaEvwghnE39+H/RJgYjhOhtah+BVge5FN9nSsMIDDA9L5ujjWRYSijwP0IIH6HRPN+FxFRXPmi++wrFQ0cpyAqFGVLKT9EUxhA0ZeoymhV3qynITLSVAk6hrQxx0nSuomwDBqMNL78JDDD5Asai+QYfRVOePNBWrbCUumgW6FS0IdWbaMOpoE2mu4M2we8Q2sSxleUVXEqZjaYQ90SzTH8OjJBS/rccyQwWQtxG+3P+ziSnj5TS3CrvJIqum1suK6XJh3gi2stBKvCGKb/SWATYopUtAvhPefIsJv9ANB/iJLQh5zloSjvAF4CbaTh9q+lFpjeasnbRJEMo2uTKkugBxJjq85/AEFnUNx0pZRjwDzSL+lU0C96QwuGKQ2iT9G6LEibpSSl/Rpto52KSJc2Uz3EK+tfeKtSe75viHwFeNn0uCCF+RVvWbZcp3mi0F4lp5vHN0v0X2uTG02gjLjtN5/L5Ae2l50+mdDOBjib/12v5H0wvpkCyqZ8/CDqa8tvF75M+zTf2WQ4MMymrSCn/jdZHNgrNxecMvy93V16+MuV1Ac1Snf/MegHNGn8b7VnzuZQyvJxpL0RbgSUZbc7CeksjSim/RvOJ/gqtf2zl9/kHfYB9hZ4FCsVDQ1SOP75CoSgLIcQ0tGWahle1LAqFouoRQnwFbJZSbi0zsOVpJqAtwxb2oNJ8GAghfkJbHvJMVcui+GPySCwur1AoFArFHx0p5RtVLcOjgpSyTVXLoPhjoxRkhUKhUCgUgOZGg7YcZXG4lbLEn0LxWKFcLBQKhUKhUCgUCjPUJD2FQqFQKBQKhcIMpSArFAqFQqFQKBRmVGsf5IYNG0oXF5eHnu+dO3ewsyttp1tFdUW17eOLatvHF9W2jzeqfR9fqqJtT5w4cUNK6VBWuGqtILu4uHD8+PGHnu++ffvo3LnzQ89XUfmotn18UW37+KLa9vFGte/jS1W0rRDikiXhlIuFQqFQKBQKhUJhRrVUkIUQfYQQy9PS0qpaFIVCoVAoFArFY0a1VJCllNullGPr1Stt11WFQqFQKBQKhaL8VGsfZIVCoVCUzr1790hMTCQrK6uqRak06tWrx9mzZ6taDEUlodr38aUy27ZWrVo4OztTs2bNCsVXCrJCoVA8xiQmJlKnTh1cXFwQQlS1OJVCRkYGderUqWoxFJWEat/Hl8pqWyklN2/eJDExkeeff75CaVRLFwuFQqFQWEZWVhYNGjR4bJVjhUKhKIwQggYNGtzXyJlSkBUKheIxRynHCoXij8b9PveUgqxQKBQKhUKhUJihFGSFQqFQVCrW1tZ4eXnh7u6Op6cnn376KXl5eYC2UUC9evXw8vLSP2FhYUXScHFxwcPDA4PBQGBgINeuXXvYxSiWRYsW8dtvvz3QNL///nu9Luzt7WnRogVeXl6MGDHigeZTGGdnZ27dulXk/MaNG3F1daVr164VTjspKYlBgwbRvHlzfHx86NWrF/Hx8cTHx2Nra1ug/devXw9Aeno6Y8aMwWAw4OPjQ0BAAJGRkVy6dInOnTvj5uaGu7s7S5Ys0fO5efMmXbp04YUXXqB79+7kLwcbExODv78/NjY2LFq0qIh8OTk5GAwG+vfvX+6ymdfP9evX6dy5M3Z2drz77rsFwuXl5REQEMDt27dLTS+/HXJycqhfvz4Aly9fZvDgweWWrST27duHu7s7Xl5eZGdnP7B0u3TpwuOyBG+1VJDVOsgKhUJRfbC1tcVoNBITE8OePXvYvXs306dP16936NABo9Gof0pSxMLDwzl16hS+vr588sknFuefm5t732UoiYooyGXJ0717d70ufH19Wb9+PUajkbVr196PqBUmNDSUVatWFfviUhw5OTkFjqWU9O/fn8DAQOLj4zlx4gQzZ84kOTkZgBYtWhRo/2HDhgEwatQonn76aaKjozlx4gShoaHcuHGDmjVrsmjRImJjYzl69CgLFy7k3LlzAMyaNYuePXty/vx5OnTowNy5cwFo2LAhn332Ge+9916xMi9YsIBWrVrdd/3Url2bWbNmMWfOnCLhtm/fjq+vL/b29uXO49lnn2XTpk0Vkq841q1bxz/+8Q+MRiNPPPHEA0v3jTfeYNmyZQ8svaqkWq5iIaXcDmz39fUdU9WyKBQKRXVh+vYYYpPSH2iabk51mdrH3eLwjo6OLF++HD8/P6ZNm1ahPDt27MjixYsBGD9+PD/99BN3797l9ddf1xVvFxcXBg8ezJ49e/jwww/JyMhg+fLlZGdn07x5c7788ktq165NcHAwtra2REVFcf36dVauXMnatWs5evQobdq0YfXq1QD88MMPTJ06lbt379KsWTNWrVrFypUrSUpKIiAggIYNGxIeHl5sOHt7+yLyXL9+nWXLllGjRg3c3NzYuHGjRWUPDQ3lzJkzuhW0R48ehISE0LZtWxo2bMi4cePYvXs3tWvXZtu2bTg6OpKcnMz48eP55ZdfsLKyYvHixbRt25aUlBTeeOMNkpKSaN++PVLKIvlNmTKFiIgIgoKCePXVV5k6dSrjxo3j5MmTuqLasWNHQkND2bFjB2lpaVhZWfHjjz/qaezZswd7e3tGjx6tn/P29gYgPj6+2HLGxcVhNBrZvHkzd+7cAaBZs2Y0a9YMACcnJwDq1q1Ly5YtuXLlCi+++CLbtm0jIiICgKCgIHr06MGsWbNo1KgRjRo1YuvWrUXyunTpkt4uS5cutagdSqqf2bNn065du2KXLlu/fj0TJ07Uj9esWcPSpUvJzs7mT3/6E0uWLMHKqni7ZXx8PK+//jpGo5HQ0FB27tzJr7/+SlJSEkFBQYSEhJCRkcGgQYNISkoiNzeXadOm8frrrxdJa9myZWzZsoUff/yRXbt2MWLECJYsWaLXzbhx42jfvj3Dhw/H2dmZ0aNHs23bNnJzc/nmm2948cUXycjIYMKECURFRQEwY8YM+vfvT79+/ejSpQsfffRRuerxUaRaWpCrlG/e4pmkH6paCoVCoai2NG3alNzcXK5fvw7AwYMHCwyx//zzz6XG37FjBx4eHoBmMdy/fz+nTp3Sv/Np0KABJ0+eZMiQIQwYMIDIyEiio6NxdXXliy++0MOlpqbqlsi+ffvy3nvvERMTw+nTpzEajdy4cYOZM2cSFhbGyZMn8fX1ZcGCBUycOBEnJyfCw8MJDw8vMVxx8syePZuoqChOnTr1wCxuaWlpdOrUiejoaPz9/Vm5ciUAEydO5MMPP+T48eNs3rxZV1SnTp1KQEAAMTExvPLKKyQlJRVJc8aMGXh5ebFp0yZmz57N4sWLsbGx4fTp03z55Ze8+eab+hB9VFSUrniZc+bMGXx8fEqUOy4urkD7HzlyhJiYGLy9vUtUGPO5cOECZ86cwc/PD9BcLBwcHABo3LgxV69eLbPe3n33XebNm1ehSV2F66c0jhw5QuvWrQGtTv79739z5MgRjEYjOTk5Fr8kARw7doytW7diNBr56quvMBqN7Nq1CxcXF6Kjozlz5gzdunUrNu64ceN45ZVXWLhwoUWjEo0aNSIqKorRo0fr/XnatGk4ODhw6tQpoqOj6dSpE6BZ6jMyMop11aluVEsLcpWScJA6dTyrWgqFQqEoN+Wx9D5MOnTowI4dO8oMFxAQgLW1NQaDgZkzZwKwefNmli1bRl5eHlevXiU2NhaDwQBQwGfzzJkzhISEcOvWLW7fvk337t31a3369EEIgYeHB40aNdKVb3d3dxISEkhMTCQ2NpZ27doBkJ2djb+/fxH5IiIiSg1nLo/BYGDYsGH079+/Qn6vxWFra0vPnj0B8PHx4eDBgwCEhYURFxenh0tNTSUzM5MDBw6wa9cuAPr162fRerSHDh3igw8+ALT6cXJy0q3AgYGBPPnkk+WWO9/FwpwtW7aUGS89PZ3XXnuNzz77rEJuCwBbt27l2WefLdH3/UGSnp5O7dq1Aa1NIiMj8fX1BSAzM5Nnn33W4rS6d++u13X//v05dOgQXbp0YdKkSUyaNIk+ffro/fB+GTBgAKD1qfz+EhYWpluchRAF2t3BwYGrV6/q/tPVFaUgl5da9amRU7qDvUKhUChK5sKFC1hbW+Po6FiuXbTCw8Np2LChfnzx4kXmz5/P3r17ee655wgODi6w7qmdnZ3+Ozg4mK1bt+Lp6cnq1avZt2+ffs3GxgYAKysr/Xf+cU5ODtbW1nTr1o0NGzaUKp+UstRw5vLs3LmTAwcOsH37dmbNmsXp06epUaPsv+QaNWroExyBAuU19yW1trbWfYGllBw7duyB+poWh3n5zHF3d7foBahwHKPRWKCs5mRnZzNgwABGjhxJ37599fMNGjQgJSUFBwcHrly5wjPPPFNqPkeOHGHLli189913ZGVlkZ6eTlBQEGvWrNHDJCQk6C8xEyZMKOAqUh7MreFSSkaNGsXHH39cobQKW7uFELi6unL8+HF27drFpEmT6NmzJ5MnTy4zrdL6FPx+f5j3qdLIysrC1tbWkmI80igXi/JiW5+a95SCrFAoFBUhJSWFcePGMWHChPtepzQ9PR07Ozvq1atHcnIyu3fvLjFsRkYGzzzzDPfu3dNXSbCUtm3bcvjwYd1SeufOHX1SWJ06dcjIyCgznDl5eXlcvnyZgIAA5syZQ1paWpkrG+Tj4uJCVFQUUkoSEhI4ceJEmXG6du1awLc231rbsWNHvvrqK0CbQJZfjtLo0KGDXn9nz57l6tWrNG/evNQ4gYGBpKen6y4fANHR0Rw+fLjEOC1atMDDw4MZM2bovtEXL15k9+7dSCkJDg7Gy8urgE8vQN++fXXlds2aNfTr169U2ebOnUtiYiIJCQmsW7eOwMDAAsoxaHWeP4GwosoxQPPmzUlISAC0Ntm8eTM3btwANNeQX375xeK0fvjhB27dusVvv/3Gtm3baNeuHVeuXMHe3p4333yTv/3tb5w8edKitJo0aUJMTAzZ2dmkpqayd+/eMuN069ZN71NSSlJTUwFtAuqNGzd47rnnLC7Lo4pSkMtJxiUr8q6V/RBRKBQKhUZmZqa+zFvXrl0JDAxk6tSp+vXCPsjffPONRel6enri7e2Nj48Pb7zxRqlDyh9//DFt2rShXbt2tGzZslzyOzg4sHr1aoYOHYrBYMDf35///ve/AIwdO5YePXoQEBBQajhzcnNzGT58OB4eHnh7ezNx4kSLh6M7depE48aNcXV15W9/+xteXl5lxlm6dCmHDx/GYDDg5ubGihUrAJg+fTphYWG0atWKHTt26BPfSuOdd94hMzMTDw8Phg0bxtq1a8u0TAsh2LZtG7t27aJZs2a4u7sTEhLC008/DRT1Qc5XvFatWsXly5fx9PSkVatWjBo1CkdHR/bv38+GDRvYs2ePHuf7778HYPLkyezcuZMXXniBAwcO6O4giYmJODs7s3jxYqZNm4azs/MDX54vH2dnZz788EO++OILnJ2ddfeWXr166SMXHh4eTJ06la5du+pLF+av6mEJfn5+9OvXD09PT4YOHYqXlxfR0dH4+fnh5eXFJ598YpH1GOD555+nf//+uLu7M2TIEN1PujSmTp1KcnIyrVq1wsvLS3fniYyMpH379mX6jlcHRHGzVqsLvr6+8vjx4w81zzhvD+yaZOO8Na7swIpqx759++jcuXNVi6GoBP6obXv27FlcXV2rWoxKJSMjwyL/WUX15HFp38TEREaPHs1//vOf+0qn8EomjxJ/+ctfGDRokD5prywqu22Le/4JIU5IKX3Lilv9VfyHjJVNDcgu2wdHoVAoFAqFIh9nZ2eCg4Mtdqepjnh7e1usHD/qPDKT9IQQbsA04Cbwo5TSsjG2h4xVLRu4lwt5uWBlXdXiKBQKhUKhqCYMGTLkvtMojx903759i/g2z58//752RSyN+/HRftSoVAVZCLES6A1cl1K2MjvfA/gnYA2ESilnAz2Bz6SUB4UQ3wGPpoJsa0NeloCsNKj9VFWLo1AoFAqFQlEs3333XVWLUG2pbBeL1UAP8xNCCGtgKZpC7AYMNVmPvwSGCCHmAQ0qWa4KY1W7Nnn3BGRV/0WwFQqFQqFQKBRFqVQLspTygBDCpdDpl4B4KeUFACHERqCflPL/AX8xKdAlrhAuhBgLjAVtdxfztSwfBg65udjmCk4c+pGMupYvyaKoHty+ffuh9ynFw+GP2rb16tWzaPmu6kxubu5jX8Y/Mqp9H18qu22zsrIq/NyvCh/kxsBls+NEoI1JkZ4M2AHzSoospVwOLAdtFYuHPSv9ysbVZF25iI97M2j2cPNWVD5/1JUO/gj8Udv27Nmzj8UKAKXxuKxyoCge1b6PL5XdtrVq1cLb27tCcR+ZSXpSygRMluGyEEL0AfqUtTh5ZWBlX0dzschULhYKhUKhUCgUjyNVsczbFcB8w3Fn0zmLkVJul1KOrVev3gMVzBKs6tYnL0fAbzcfet4KhUJRHbG2ttY3CvH09OTTTz/Vt7bdt28f9erVK7BRRFhYWJE0XFxc8PDw0DdVuHbt2sMuRrEsWrTogW848f333+t1YW9vT4sWLfDy8mLEiBEPNJ/CODs7c+tWUePPxo0bcXV1va+VD5KSkhg0aBDNmzfHx8eHXr16ER8fT3x8PLa2tgXaP3+nvvT0dMaMGYPBYMDHx4eAgAAiIyO5dOkSnTt3xs3NDXd3d5YsWaLnc/PmTbp06cILL7xA9+7dSUtLAyAmJgZ/f39sbGyKXT84JycHg8GgbyldHszr5z//+Q+tW7fGw8MDHx+fAsP7eXl5BAQEcPv2beLj4y3a5MUSTp48WeG1lbt3716pLg6ZmZm8/PLL5doAyBIWLVrE2rVrH1h6xSKlrNQP4AKcMTuuAVwAngejbBhjAAAgAElEQVSeAKIB93Km2QdY3rx5c/mwub5okYxt0ULm7f77Q89bUfmEh4dXtQiKSuKP2raxsbFVLYK0s7PTfycnJ8suXbrIKVOmSCm1dunVq1eZaTRp0kSmpKRIKaX8+9//Lt955x39Wnp6eqlxc3JyKiK2RZjLZSnlkadTp04yMjKyvGJViMaNG8vU1NQi57t06SKPHj1qcTr37t0rcJyXlyf9/PzkihUr9HMnT56Uhw4dkufPn5eenp7FpvPaa6/JkJAQmZaWJqWUMj4+Xu7atUteuXJFRkVFSSmlTEtLk02bNpVxcXFSSinfe+89OW/ePCmllB9//LGcPHmylFLKa9euycjISPnRRx/JhQsXFslrzpw5cujQobJfv34WlzMf8/o5ceKETEpKklJKaTQapbOzsx5u69at8n/+53+klLLUcpeXFStWyL/+9a8PJK2KUrjN8zl48KDs3r17ifHKundLIiMjQ7Zu3brMcMU9/4Dj0gJds1ItyEKIDcBRoIUQIlEI8ZaUMgeYAHwPnAU2SyljypOurEoLsp0dIJDJ8Q89b4VCobgvdk+CVb0e7Gf3pHKJ4OjoyPLly1myZEm+waPcdOzYkfh47Rk8fvx4OnXqhLu7e4Htq11cXPjoo49o3bo1X3/9NStWrMDPzw9PT09ee+013eobHBzM+PHjadu2LU2bNmXfvn2MGjUKV1dXgoOD9fR++OEH/P39ad26NQMHDuT27dssXryYpKQkAgICCAgIKDFccfIsXrwYNzc3DAZDudbGDQ0N5d1339WPe/TowaFDh8jJyaF+/fpMmjQJT09P/P39uX79OgDJyckMGDAAX19fXnrpJSIiIgBISUmhW7duuLu78/bbbxfbHlOmTCEiIoKgoCAmTZpEZmYmQUFBeHh40Lp1aw4cOKDL1b9/fwICAujevXuBNPbs2YO9vX2BNXK9vb1L3Ro8Li4Oo9HI9OnTEUIA0KxZM3r27ImTk5Nufa1bty4tW7bkyhVtIHrbtm0EBQUBEBQUxNatWwFtUr+vry81ahT1LL106RJ79uxh5MiRpVV9sRSun9atW/PMM88A2nbSt2/f5t69ewCsX7+efv366XHv3bvHkCFDcHV1ZdCgQWRmZgLads2dOnXCx8eHnj176ltQt2/fnkmTJvHSSy/RokULjhw5QmZmJjNmzGD9+vWlWmkzMjIICgrCYDBgMBj0ejEfNZg6dSotWrSgQ4cODB48WLe0L1u2TL93Bg4cqMs5fPhwxo8fz0svvVTsttZJSUkEBwdz9OhRvLy8SEhIKJBfREQEffv2BSAkJIS33nqLTp060bRpU327cdC2HDcYDHh6euptZG9vj5OTEydPnix3m1lKpSrIUsqhUspnpJQ1pZTOUsovTOd3SSlflFI2k1LOqkwZHjTWT2kr0OUkXapiSRQKhaJ60rRpU3Jzc3UF7uDBgwWG2H/++edS4+/YsQMPDw8AZs2axf79+zl16pT+nU+DBg04efIkQ4YMYcCAAURGRhIdHY2rqytffPGFHi41NZWjR4+ycOFC+vbty3vvvUdMTAynT5/GaDRy48YNZs6cSVhYGCdPnsTX15cFCxYwceJEnJycCA8PJzw8vMRwxckze/ZsoqKiOHXqFMuWLXsg9ZqWlkanTp2Ijo7G39+flStXAjBx4kQ+/PBDjh8/zubNm3VFderUqQQEBBATE8Mrr7xCUlJSkTRnzJiBl5cXmzZtYvbs2SxevBgbGxtOnz7Nl19+yZtvvkl2djYAUVFRbNmyhR9//LFAGmfOnMHHx6dEuePi4gq0/5EjR4iJicHb2xsrq9LVlAsXLnDmzBn8/PwAzcXCwcEBgMaNG3P16tUy6+3dd99l3rx5uiJeHgrXjzmbN2+mTZs21KxZE4AjR47QunVr/XpsbCzvvvsuZ8+epVatWvzrX//i7t27/PWvf+Xbb7/lxIkTDB8+nH/84x96HCklx44dY968ecyYMQNbW1umTJnCsGHDMBqNvP7668XKOW3aNBwcHDh16hTR0dFFdruLiIhgx44dnDp1ip07dxIZGalfGzhwoH7vNGvWjNWrV+vXrl69SkREBHPnzi2Sp5OTE8uWLSMgIACj0YiLi0updXnu3Dn27NlDREQEU6ZMITc3l+joaObMmcO+ffuIjo7m008/1cP7+vpy8ODBUtO8Hx6ZSXrloSon6dVwdAQg59d0nnjouSsUCsV90HN22WGqgA4dOrBjx44ywwUEBGBtbY3BYGDmzJmApoQsW7aMvLw8rl69SmxsLAaDAYDBgwfrcc+cOUNISAi3bt3i9u3bBaycffr0QQiBh4cHjRo10pVvd3d3EhISSExMJDY2Vrd4Zmdn4+/vX0S+iIiIUsOZy2MwGBg2bBj9+/evkN9rcdja2tKzZ08AfHx8dOUhLCyMuLg4PVxqaiqZmZkcOHCAXbt2AdCvXz+LVhM4dOgQH3zwAaDVj5OTk27NDwwM5Mknnyy33C1atMBoNBY4t2VLiau96qSnp/Paa6/x2WefYW9vX+58AbZu3cqzzz5bou97RTl9+jQhISHs2bNHP5eenk7t2rX14+eff562bdsCmjV2+fLldO7cmZiYGN3fOzc3F2dnZz3OgAEDAK19ExISLJYnLCxMtxoLIYq006FDh+jfvz82NjbY2NjQu3dv/dqpU6eYMmUKt27dIiMjo8C1gQMHlvkSYym9e/fmiSeewNHRkaeeeoqUlBT27t3L4MGDeeopbWO2/G/QRqPKUwflpVoqyFLK7cB2X1/fMQ877xqmN9OctDsPO2uFQqF4LLhw4QLW1tY4Ojpy9uxZi+OFh4fTsGFD/fjixYvMnz+fvXv38txzzxEcHExWVpZ+3c7OTv8dHBzM1q1b8fT0ZPXq1QUmT9nY2ABgZWWl/84/zsnJwdramm7durFhw4ZS5ZNSlhrOXJ6dO3dy4MABtm/fzqxZszh9+nSxw/+FqVGjhj7BEShQ3iee+N1sY21tTU5Oji7XsWPHClyvDMzLZ467u7tFL0CF4xiNxgJlNSc7O5sBAwYwcuRIfZgeNCt9SkoKDg4OXLlyRXd3KIkjR46wZcsWvvvuO7KyskhPTycoKIg1a9boYRISEvSXmAkTJpS5nfIvv/zCgAEDWLduHc8//7x+vrAiWdhiLYRASonBYCjRMprfP83bt7IZMWIEu3fvplWrVoSGhuouOlBym5eEef8177tAgXvPkvJlZWVha2tbrvzLQ1WsYlGtqeFoUpAzciAnu4qlUSgUiupFSkoK48aNY8KECRUa0jYnPT0dOzs76tWrR3JyMrt37y4xbEZGBs888wz37t3TV0mwlLZt23L48GHdUnrnzh3OnTsHQJ06dfRVAEoLZ05eXh6XL18mICCAOXPmkJaWpvsql4WLiwtRUVFIKUlISODEiRNlxunatWsBn858a23Hjh356quvANi+fbtFqxl06NBBr7+zZ89y9epVyhrNDQwMJD09XXf5AIiOjubw4cMlxmnRogUeHh7MmDFD942+ePEiu3fvRkpJcHAwXl5eTJw4sUC8vn376srtmjVrCvj8FsfcuXNJTEwkISGBdevWERgYWEA5Bq3OjUYjRqOxTOU4NTWVXr16MX/+fN06nE/z5s0LWDwvXryouzJ89dVXtG/fHjc3N65cucKxY8cA7UUgJqb0aVrmfbAkunXrpvcBKSWpqakFrrdr147vvvuOu3fvkpGRoY8sgNaPn376ae7du6f3l4ri4uKi99lvv/22zPAvv/wymzZt4tdffwXQv0FzyWjVqtV9yVMa1VJBFkL0EUIsz1++5WFiXb8+WFuRk2WltptWKBQKC8jMzNSXeevatSuBgYEFJtQV9kG2dDkoT09PvL298fHx4Y033ih10tfHH39MmzZtaNeuHS1btiyX/A4ODqxevZqhQ4diMBjw9/fnv//9LwBjx46lR48eBAQElBrOnNzcXIYPH46Hhwfe3t5MnDiR+vXrWyRLp06daNy4Ma6urvztb3+zaKmwpUuXcvjwYQwGA25ubqxYsQKA6dOnExYWRqtWrdixYwdOTk5lpvXOO++QmZmJh4cHw4YNY+3atWVapoUQbNu2jV27dtGsWTPc3d0JCQnh6aefBor6IOcrcqtWreLy5ct4enrSqlUrRo0ahaOjI/v372fDhg3s2bNHj/P9998DMHnyZHbu3MkLL7zAgQMHdHeQxMREnJ2dWbx4MdOmTcPZ2fmBL88H8M9//pOLFy8ydepUXbabN7VlYXv16lVg5MLV1ZUFCxbg6urKb7/9xtixY7GxseGbb77h/fffx2Aw4O3tzU8//VRqni+//DLR0dF4e3uXeO9MnTqV5ORkWrVqhZeXVxELtb+/Pz169MDDw4NXXnkFDw8P8hdCmDFjBn5+frRr1w43N7f7qB3NF/rPf/4zfn5+Fo1oeHp68uGHH9KxY0e8vLz09gQ4evQoXbp0uS95SkNUdBbxo4Cvr688fvz4Q8831q819R1u4LQmDBxefOj5KyqPP+pua38E/qhte/bsWVxdXatajEpF7bT2ePO4tG9iYiKjR4+u8JrFlc3t27ext7fnzp07tG/fnjVr1uj+/JVFRds2MjKSzz//nFWrVpUarrjnnxDihJTSt6w8qqUPcpVjZ0vuXWVBVigUCoVCYRnOzs4EBwfriuijxltvvUVcXBxZWVmMGjWq0pXj++HXX39l+vTplZpHtVSQq3IVC4A8eztyb1nBnRtVkr9CoVAoFIrqR3nWvK4IoaGhBXYWBM3XfPHixWXG3bRpU5XkWxEKr7VdGVRLBbkqV7EAyLWvR26yFdx+NLY6VSgUCoVCoRg9enSZEwkfp3wrk2o5Sa+qyalbX3OxuH29qkVRKBQKhUKhUDxglIJcAfLq1CM32wp5q+wdehQKhUKhUCgU1YtqqSBX5TJvALkODoAgO+FCleSvUCgUCoVCoag8qqWCLKXcLqUcm79G38Mmp1EjALIrcYtDhUKhUCgUCkXVUC0V5Komt5EjAHev/gr3ssoIrVAoFH9srK2t9Y1CPD09+fTTT/XtZvft20e9evUKbBQRFhZWJA0XFxc8PDwwGAwEBgZy7dqjMUl60aJFD3zDie+//16vC3t7e1q0aIGXlxcjRox4oPkUxtnZmVu3ii5funHjRlxdXenatWuF005KSmLQoEE0b94cHx8fevXqRXx8PPHx8dja2hZo//yd+tLT0xkzZgwGgwEfHx8CAgKIjIzk0qVLdO7cGTc3N9zd3QusnnDz5k26dOnCCy+8QPfu3ckfaY6JicHf3x8bGxsWLVpURL6cnBwMBoO+pXR5MK+fo0eP4unpiZeXF56enmzbtk0Pd+fOHTp37lzs9tnDhw9n69atAIwcOZK4uLhyy3G/JCcn4+fnh7e3N0eOHHlg6b733nscOHDggaX30JBSVtuPj4+PrArCw8Pl+fZtZWK3JlJePV0lMigqh/Dw8KoWQVFJ/FHbNjY2tqpFkHZ2dvrv5ORk2aVLFzllyhQppdYuvXr1KjONJk2ayJSUFCmllH//+9/lO++8o19LT08vNW5OTk5FxLYIc7kspTzydOrUSUZGRpZXrArRuHFjmZqaWuR8ly5d5NGjRy1O5969ewWO8/LypJ+fn1yxYoV+7uTJk/LQoUPy/Pnz0tPTs9h0XnvtNRkSEiLT0tKklFLGx8fLXbt2yStXrsioqCgppZRpaWmyadOmMi4uTkop5XvvvSfnzZsnpZTy448/lpMnT5ZSSnnt2jUZGRkpP/roI7lw4cIiec2ZM0cOHTpU9uvXz+Jy5mNeP3fu3NHLf+XKFeno6Chzc3OllFIuWrRILlmypNg0hg0bJv/973+XO+8HyZdffinffvvtB55ufHy87NGjR7HXyrp375finn/AcWmBjlktl3l7FKjl1oLMEzfgxjl4uvL2AlcoFIoHxZxjc/jvr0W3Pr4fWj7Vko9e+sji8I6Ojixfvhw/Pz+mTZtWoTzN11cdP348P/30E3fv3uX111/XNw9wcXFh8ODB7Nmzhw8//JCMjAyWL19OdnY2zZs358svv6R27doEBwdja2tLVFQU169fZ+XKlaxdu5ajR4/Spk0bVq9eDcAPP/zA1KlTuXv3Ls2aNWPVqlWsXLmSpKQkAgICaNiwIeHh4cWGs7e3LyLP9evXWbZsGTVq1MDNzY2NGzdaVPbQ0FDOnDmjW0F79OhBSEgIbdu2pWHDhowbN47du3dTu3Zttm3bhqOjI8nJyYwfP55ffvkFKysrFi9eTNu2bUlJSeGNN94gKSmJ9u3bI4vZWXfKlClEREQQFBTEq6++ytSpUxk3bhwnT56kZs2aLFq0iI4dOxIaGsqOHTtIS0vDysqKH3/8UU9jz5492NvbF1gGzNvbG4D4+PhiyxkXF4fRaGTz5s3cuXMHgGbNmtGsWTMAfVvsunXr0rJlS65cucKLL77Itm3biIiIACAoKIgePXowa9YsGjVqRKNGjXQrrTmXLl3S2yV/m2tLKVw/s2fP1q9lZmYC6PW6fv16tmzZAkBeXh4TJkxg7969PPvss1hbW+vx2rdvz5IlS2jVqhUjR47EaDQipWTs2LFMnDiRc+fOMW7cOG7evIm1tTVbtmwhPj6eJUuW6OUbN24c7du3Z/jw4XzwwQfs3LmTGjVq0LNnT+bMmVOkHMePH2fy5MlkZmYSERHBsWPHcHR01EcUNm7cSFhYGKGhoQwfPpwGDRoQGRnJtWvX+PTTT3n11VcB+OSTT9iwYQNWVlb07t2bWbNm0axZM65evUpKSgoODg7lqt+qRCnIFaSW90tk7P+J3EtnsG41oKrFUSgUimpD06ZNyc3N5fp1banMgwcP4uXlpV//9ttvdUWoOHbs2IGHhwcAs2bNombNmtSuXZsuXbpw6tQpfQewBg0acPLkSUAbeh8zRls6PyQkhC+++IJ33nkHgNTUVI4ePcp3331H3759OXz4MKGhofj5+WE0GnF2dmbmzJmEhYVhZ2fHnDlzWLBgAVOmTGHBggWEh4fTsGFDbty4UWK4wvI4OTlx8eJFbGxsinVrqAhpaWl06tSJ2bNn8/7777Ny5UomTZrExIkT+fDDD2nbti0JCQn07t2bM2fOMHXqVAICApg8eTLbtm1j+fLlRdKcMWMGe/fuZcmSJXh5eTFnzhxsbGw4ffo0MTExvPLKK5w/fx6AqKgojEYjTz75ZIE0zpw5g4+PT4lyx8XFFWj/zz//nGvXruHt7Y2VVemeoBcuXODMmTP4+fkBWjvnK2GNGzfm6tWyV5t69913mTdvHjdulH/zr8L1A3DkyBHGjBnDpUuX+Oqrr7C2tiYrK4vExEScnZ0B+Oabb7h48SKxsbEkJSXh5ubGuHHjCqR94sQJbty4wenTpwH0fjJ06FCmTZtGnz59yMrKIi8vr8QXjeTkZHbt2kVMTAxCiBL7mq+vL1OmTNFfvnJyckot9/Xr1zl8+DCnT59m0KBBvPrqq2zfvp3du3dz7NgxbG1t+fXXX/Xw+W4b/fr1s6BWHw2qpYJc1TvpAdh6am+/WaejsetVZWIoFAqFxZTH0vsw6dChAzt27CgzXEBAANbW1hgMBmbOnAnA5s2bWbZsGXl5eVy9epXY2FhdQR48eLAe98yZM4SEhHDr1i1u375dYCeuPn36IITAw8ODRo0a6cq3u7s7CQkJJCYmEhsbS7t27QDIzs7G39+/iHwRERGlhjOXx2AwMGzYMPr3718hv9fisLW1pWfPngD4+Phw8OBBAMLCwgr4tKamppKZmcmBAwfYtWsXAP369aNOnTpl5nHo0CE++OADQKsfJycnXTkLDAwsohxbQosWLTAajQXO5VtaSyM9PZ3XXnuNzz77rMJbN2/dupVnn322RN/3ivCnP/2JmJgYYmJiGDVqFD169OD69es89dRTepgDBw4wdOhQrKyscHZ2pnPnzkXSad68OXFxcUycOJFevXoRGBhIamoqN27coE+fPgDUqlWrVFmeeuoprKysGDNmDL169aJ3794PpIz9+/dHCIHBYODKlSuA1s9GjRqFra2tnnc+jo6OJCUlPZC8HxbVUkGWVbyTHkAtV1cAsn5OwK6qhFAoFIpqyIULF7C2tsbR0ZGzZ89aHC/fUpvPxYsXmT9/Pnv37uW5554jODiYrKzfJ07b2f3+dA4ODmbr1q14enqyevVq9u3bp1+zsbEBwMrKSv+df5yTk4O1tTXdunVjw4YNpconpSw1nLk8O3fu5MCBA2zfvp1Zs2Zx+vRpatQo+y+5Ro0aBSZ5mZf3iSee0H9bW1vrVkApJceOHStwvTIwL5857u7uFr0AFY5jNBqLndAG2svHgAEDGDlyJH379tXPN2jQQB/Kv3LlCs8880yp+Rw5coQtW7bw3XffkZWVRXp6OkFBQaxZs0YPk5CQoL/ETJgwweId49zd3bGxsSE2NpbGjRsXaCtLaNCgAadOnWL37t0sXbqUb7/9tlj3CCi5X9SsWZPjx4+zZ88evv76a/7v//6PH374ocy8raysCrjcFJbd/D4pzjWnMFlZWbriXF1Qq1hUEOv69bGqVYN711OhhBtYoVAoFAVJSUlh3LhxTJgwASHEfaWVnp6OnZ0d9erVIzk5md27d5cYNiMjg2eeeYZ79+7pqyRYStu2bTl8+LBuKb1z5w7nzp0DoE6dOmRkZJQZzpy8vDwuX75MQEAAc+bMIS0tjdu3b1ski4uLC1FRUUgpSUhI4MSJE2XG6dq1awHf2nxrbceOHfnqq68A2L59u16O0ujQoYNef2fPnuXq1auUNZobGBhIeno6K1eu1M9FR0dz+PDhEuO0aNECDw8PZsyYoStgFy9eZPfu3UgpCQ4OxsvLi4kTJxaI17dvX125XbNmTZlD+nPnziUxMZGEhATWrVtHYGBgAeUYtDo3Go0YjcYyleOLFy+Sm5ur/z5//jxNmjTBwcGBzMxMsrOzAa3uN23aRF5eHleuXGH//v1F0kpJSUFKycCBA5kxYwYnT57kySefxMHBge3btwOa4vnbb7/RpEkTYmJiyM7OJjU1lb179wJav09PT6d3794sXLiQqKioUuXPx8rKiieffJLz58+Tl5fHv//97zLjdOvWjZUrV+q+1+YuFufOnaNVq+o1X6taWpCripzsXA5+fZ60bAmdoaZDfe5l3Ib0RKj/XFWLp1AoFI8kmZmZeHl5ce/ePWrUqMGbb77J+++/r18v7IMcEhLC66+/Xma6np6eeHt74+PjQ5MmTXTXhuL4+OOPadOmDQ4ODrRp08YiZTAfBwcHVq9ezdChQ7l79y4AM2fO5MUXX2Ts2LH06NEDJycnwsPDSwxnTm5uLsOHDyctLQ0pJRMnTqR+/foWydKpUycaN26Mq6sr7u7uBeqtJJYuXcr48eNZtWoVOTk5BAQEsHTpUqZPn87QoUNZt24d7dq10ye+lcY777zD22+/jYeHBzVr1mTt2rVlWqaFEGzbto2//vWvzJo1i1q1atG0aVMWLVqElLKID/KYMWP4y1/+wqpVq3j//ffx9PTEzs4OBwcH5s+fz/79+9mwYQMGg0GPN2fOHLp3787kyZMZNGgQ//rXv3j++efZtGkTAImJibRt25b09HSsrKyYP38+586do3bt2mWWuTzs37+fefPmUbNmTaytrfnXv/6lu5107dqVI0eO0LlzZ15//XXCw8Nxc3PjueeeK9Zl5/Lly7z11ltIKRFC6Nbj9evX8/bbb/O///u/PPHEE3z77bc8//zz9O/fH3d3d5o2bUrr1q0BzS99wIAB3L17l7y8PBYsWGBxWfLr1NHRER8fH71Pl0Tv3r2Jjo7G19eXmjVr0qdPHz7++GPu3r1LQkKCPjGzuiAsMY0/qvj6+srjx48/tPyklGyYcYyse3cYNfNlLgcN5l7cCZquWwbNK74+pOLRYd++fcX6gimqP3/Utj179iyuJpewx5WMjAyL/GcV1ZPHpX0jIyP5/PPPWbVqVVWL8lD5+uuviY2NZerUqUWuVXbbFvf8E0KckFL6lhVXuViUAyEET3vWJfMGpKX8Rs2mL5KdYY289mCXTVIoFAqFQvF44efnR/v27Uv0q35ckVLy3nvvVbUY5UYpyOUg9bfbTL76VySSuJ+SsfVpi8y1IuvMyaoWTaFQKBQKxSPOW2+9VebSdZXNjBkzCuxc6OXlVWD95gfNoEGDqFu3bqWlX1lUSx/kqlrm7cna9tSp04SkOj8Te7QWXu9o6zreMZ6jes3NVCgUCoVC8UdkypQp+trcipKplhZkKeV2KeXYevXqPfS8x3mP4JzjMe7cvMuvv9li62xHRkzqQ5dDoVAoFAqFQlE5VEsFuSoZ4NaeX+peI0fc4+zRq9h7OJOVkkdOqlKSFQqFQqFQKB4HlIJcToQQeNu34eJTp4g9dgUbd235nsxjEVUsmUKhUCgUCoXiQaAU5ArQ/2k/zjc8BXcFyfbaun53YyxbfFuhUCgUCoVC8WijFOQKYGtdiwZNXLhd8xZH4hpQo3YOd89GV7VYCoVC8UhibW2Nl5cX7u7ueHp68umnn+pLXe3bt4969eoVmFEfFhZWJA0XFxc8PDwwGAwEBgZy7dq1h12MYlm0aBG//fbbA03z+++/1+vC3t6eFi1a4OXlxYgRIx5oPoVxdnbm1q1bRc5v3LgRV1dXunat+Hr/SUlJDBo0iObNm+Pj40OvXr2Ij48nPj4eW1vbAu2fv1Nfeno6Y8aMwWAw4OPjQ0BAAJGRkVy6dInOnTvj5uaGu7s7S5Ys0fO5efMmXbp04YUXXqB79+6kpaUBEBMTg7+/PzY2NixatKiIfDk5ORgMBn1L6fJQXP1cvHgROzu7AnnduXOHzp07V8kyb6Ghobz77rsPPN2QkJBi67M4YmNj9c19EhISHpgMAwcO5MKFCw8svXyUglxB/t4hmDPPHCDjag2yGqu5QzQAACAASURBVDmTFfezRfuRKxQKxR8NW1tbjEYjMTEx7Nmzh927dzN9+nT9eocOHfStfI1GY4mKWHh4OKdOncLX15dPPvnE4vzzt/6tDCqiIJclT/fu3fW68PX1Zf369RiNRtauXXs/olaY0NBQVq1aVeyLS3Hk5OQUOJZS0r9/fwIDA4mPj+fEiRPMnDmT5ORkQNtW2rz9hw0bBsCoUaN4+umniY6O5sSJE4SGhnLjxg1q1qzJokWLiI2N5ejRoyxcuFDf0nvWrFn07NmT8+fP06FDB+bOnQtAw4YN+eyzz0pcj3fBggUV3gq5uPp5//336dmzZ5FwAwcOLLLMW+H6qiqklJWqvG/ZsoWhQ4cSFRWFi4vLA0t33LhxzJs374Gll49SkCuIq0Mz0p/LJNvqLj+79CX7+h3u/ldtGKJQKB5drn3yCZfeHPFAP9fKoagCODo6snz5cpYsWVJho0LHjh2Jj48HYPz48XTq1Al3d/cCO3W5uLjw0Ucf0bp1a77++mtWrFiBn58fnp6evPbaa7pSGxwczPjx42nbti1NmzZl3759jBo1CldXV4KDg/X0fvjhB/z9/WndujUDBw7k9u3bLF68mKSkJAICAggICCgxXHHyLF68GDc3NwwGA0OGDLG47IUtgT169ODQoUPk5ORQv359Jk2ahKenJ/7+/ly/fh2A5ORkBgwYgK+vLy+99BIREdqcmZSUFLp164a7uztvv/12se0xZcoUIiIiCAoKYtKkSWRmZhIUFISHhwetW7fmwIEDulz9+/cnICCA7t27F0hjz5492NvbM3r0aP2ct7d3qVuDx8XFYTQamT59OkIIAJo1a0bPnj1xcnLSt5iuW7cuLVu25MqVKwBs27aNoKAgAIKCgti6dSsAjRo1wtfXlxo1iq5ue+nSJfbs2cPIkSNLq/piKVw/AN988w0tW7akZcuWBcKuX7+efv36ARAWFkbnzp3p3bs3Hh4eAMydO5dWrVrRqlUrPvvsM0Dbaa5nz554enrSqlUrvvnmG0Dbla9Tp074+PjQs2dP/WWjffv2TJo0iZdeeokWLVpw5MiRAuXs1KkTL7zwAjNnzoT/z959x/d0/Q8cf53sREhElghiNSTyyURCNYkRe9SetZUWRX9U1R6ttlapUbRGKUXt1VJi1k6QiJESJDYhQSKSnN8fHz7fBBk0kYTzfDw+D7k3997zvvd+8P6cz/ueA0RGRuLi4kLHjh1xdXXl2rVrLF26FDc3NypXrszw4cN1+2/evBkvLy/c3d0JCgp64VrMmTOHRo0akZiY+MLvNmzYwI8//sjMmTOpU6cOkZGR6aYXnzRpki6mjM4hOTmZQYMGUblyZTQaDbNnzwYgICCAbdu25fgH4QI5DnJ+MbzWx/wSuQ7D6zUpbWxJYsghTN7yKV0VRVH+q7Jly5KSkqJL4Pbu3ZvuP8s//viDcuXKZbj/pk2bdEnFxIkTMTQ0xMzMjNq1a3Py5Ek0Gg0AxYoV4/hx7UROd+7coVevXoD2a+Gff/6Z/v37AxAbG8s///zDhg0baNq0Kfv372fBggVUqVKF0NBQHB0dmTBhAjt27KBQoUJ8++23TJ06lVGjRjF16lR27dqFtbU1t2/fznC75+NxcHDg4sWLGBsbv7Ss4XXcv38ff39/Jk2axODBg/nll18YNmwYAwYMYOjQofj6+hIVFUXjxo0JCwtj9OjRBAYGMnz4cNavX8+8efNeOOa4cePYuXMnP/74Ix4eHnz77bcYGxtz6tQpwsPDadiwIefPnwcgJCSE0NBQihYtmu4YYWFheHt7Zxj32bNn093/2bNnc/36dTw9PbOcVOPChQuEhYVRpUoVQHufbWxsAChRogTXrl3L8roNHDiQ77//ntu3b2e57fOevz7x8fFMmTKFv//+O923HImJiURHR+Po6Khbd/ToUU6fPk2pUqU4dOgQy5Yt48iRIyQnJ1O1alUCAgI4ffo0Tk5ObN26FdDe48ePH/PZZ5+xYcMGrK2tWbZsGSNHjtTdPyklhw8fZsOGDYwbN45t27YBcPjwYcLCwjAyMqJKlSo0btwYc3Nzzpw5w5IlS/Dx8SE6OpoRI0Zw9OhRLCwsqFOnDps2bcLHx4e+ffuyd+9eSpcuzd27d9Ndh+nTp7N7927Wrl2LkZHRC9epadOmHD58GGtrawYOHKj7gJuRl53DnDlzuHr1KidOnEBfX18Xg76+Pk5OToSFheHu7v7K9zAj+SZBFkKUAmYAd4FzUsrcm9Ylh1Rz8OL7snOR1wXRjgE4nj4MdM3rsBRFUV7KPk1vUH5Ss2ZNNm3alOV2gYGB6Ovro9FodL1NK1euZO7cuaSmpnLt2jVOnz6tS5Dbtm2r2zcsLIwRI0Zw7949Hjx4kK6Xs0mTJgghcHNzw87OTpd8u7q6EhUVRXR0NKdPn9b1eCYlJeHn5/dCfAcPHsx0u7TxaDQaOnbsSPPmzV+r7vVlTE1NdV/re3t7s3fvXkDbW3n27FnddrGxsSQkJLBnzx62bNkCQLNmzShcuHCWbezbt48hQ4YA2uvj4OCgS3aCgoJeSI6z41mJRVpr1qzJcr+4uDhatmzJzJkzMTc3f+V2AdatW0fJkiUzrH1/VSNHjmTIkCGYmZmlW3/z5k2srKzSrfPz86NUqVKA9rq2bNkSU1PttGPNmzdn7969BAYGMmzYMIYNG0aTJk2oUaOGrlzpWSlSSkpKusS7RYsWgPY9kLbWt169err707x5c/bt20f9+vUpV64cPj4+ABw6dIhatWphbW0NQIcOHdizZw9PnjwhMDCQ0qVLA6Q7l4ULF1K6dGnWrFnz0h761/Gyc9ixYwcDBw5EX1//hRhsbW25evVqwUmQhRC/AI2Bm1LKymnW1wd+APSBBU+TYTdgtZRyqRDi99yMKycNCOzM2rOHMEx+nweX5mGT1wEpiqLkcxcuXEBfXx9bW1siIiKyvd+zntpnLl68yOTJk9m5cyelSpWia9eu6b7eLVSokO7nrl27sm7dOtzd3Vm0aBHBwcG63xkbGwOgp6en+/nZcnJyMvr6+tStW5fly5dnGp+UMtPt0sazefNm9uzZw8aNG5k4cSKnTp3KVnJhYGCQrk407fmm7bnT19fX1bY+6417Wc9eTkp7fmm5urpm6wPQ8/uEhoZmWBOblJREixYt6NatG02bNtWtL1asGLdu3cLGxoaYmBiKFy+eaTsHDhxgzZo1bNiwgcTEROLi4ujSpQuLFy/WbRMVFaX7ENOvX790pSLPO3z4MOvWrWPw4MHcu3dP955q1arVC6UHGV2vtCpVqsTRo0fZsmULw4YNo0GDBjRo0ACNRqP7APS8Z+/htO8BQFeq8vxyduLIjJubGyEhIcTExOgS6Ky87H2c9v2f0TlkJDExUffhIqfkdg3yIqB+2hVCCH1gFtAAcAHaCyFcgINADyHETmBbLseVY2o6vs/VsudIMTAl4n555JMneR2SoihKvnXr1i369OlDv379XvgP+1XFxcVRqFAhLCwsuHHjhu5r6JeJj4+nePHiPHnyRDdKQnb5+vqyf/9+XU/pw4cPdQ+FFS5cmPj4+Cy3Sys1NZUrV64QGBjIt99+y/3793W1yllxcnIiJCQEKSVRUVEcO3Ysy33q1KnDrFmzdMvPems/+OADfvvtNwA2btyoO4/M1KxZU3f9IiIiuHbtGuXLl890n6CgIOLi4vjll190606cOMH+/fsz3MfZ2Rk3NzfGjRunq42+ePEiW7duRUpJ165d8fDwYMCAAen2a9q0qS65Xbx4sa7mNyPfffcd0dHRREVFsXTpUoKCgtIlx6C95s8eIMwsOQZtwh0VFUVUVBT9+vVj1KhR9O3bFxsbGxISEkhKSnrpfjVr1mTt2rUkJCTw4MED1q9fT82aNYmJicHc3JzOnTvz+eefc/z4cVxcXIiJieHw4cOA9sNCeHh4pnGBtj7+3r17PHr0iPXr17+0BrxatWrs2rWLO3fukJyczIoVK/D396d69ers2rWLS5cuAaQrsfDx8WH27Nk0adIk26PL2Nvbc/XqVWJjY0lMTGTz5s1Z7lO3bl3mzp2rqzVOG8P58+dxdXXNVtvZlasJspRyD9qSibSqApFSygtSyiRgBdAM6AaMllLWAhrlZlw5SQhB11ofEmt4gkt2dTi96q+8DklRFCVfSUhI0A3zVqdOHYKCgtI9UPesBvnZ69mDSFl5NmSUt7c3HTp0yPShr/Hjx1OtWjVq1KjxwsNTWbGxsWHRokW0b98ejUaDn58fZ54+lN27d2/q169PYGBgptullZKSQqdOnXBzc8PT05MBAwZgaWmZrVj8/f0pUaIElSpV4vPPP09Xu5uRWbNmsX//fjQaDS4uLsyfPx+AsWPHsmPHDipXrsymTZtwcHDI8lj9+/cnISEBNzc3OnbsyJIlS7LsmRZCsH79erZs2UK5cuVwdXVlxIgR2NvbA/+rQX72epbML1y4kCtXrugeUOvevTu2trbs3r2b5cuXs337dt0+f/75JwDDhw9n8+bNVKhQgT179ujKQZ7V/86YMYMxY8bg6OiY48PzZaVOnTrpHppLq2rVqrRv354qVarg6+tL3759cXNz48SJE1SpUgUPDw++/vprhg8fjrGxMatXr2bw4MFoNBo8PT05dOhQlu1XqVKFZs2a4e7uTvv27V/63nF0dGT8+PEEBATg4eGBr68vjRo1ws7Ojjlz5uj2fzbSyDPPat8bNWr0Qn3yy5iYmDB8+HD8/f0JCgrCxcUly30+/vhj7O3t0Wg0uLu7s3LlSkA7hKCFhYWu9jyniNwemkwI4QRselZiIYRoBdSXUvZ8utwZqAbMBcYAt4EHUsr/y+B4vYHeAHZ2dt4rVqzI1fhf5sGDB+nqnVJkCgvOTKNmSH+ME85Tprsrev+xZ0TJG8/fW+Xt8a7eWwsLiyx7+Aq6lJQUXV2i8vZ5W+7vs6Hq5syZk9eh5Bs5cW9/+OEHrK2tX0jaQTtKx7OxsJ8JDAw8JqX0yeq4+eYhPSllGNAqG9vNA+YB+Pj4yICAgFyO7EXBwcE8325FF1vWHF5CYqEGRIfe56NBOfPQhfJmvezeKm+Hd/XeRkREZOvhq4IsPj7+rT/Hd9nbcn8DAgL4999/KVSoUJajc7wrcuLeOjg40KVLl5cm2iYmJnh6er7WcfPiDsUAJdMsOz5dl21CiCZCiHnPfyrISyXs3HD1OoBBUiwJJ+KJvJF1LZeiKIqiKO+OHj16vPXJcZ8+fdKVzHh4eOTqJDfdu3fPlW8Y8qIH+QhQQQhRBm1i3A7o8CoHkFJuBDb6+Pj0yoX4XlutirW5eWQ1d8x6Mf3H1Uwe9REmhgX/ayFFURRFUZTsmDt3bl6HkCNy9WOMEGI58A/gLISIFkL0kFImA/2AP4EIYKWUMuvHL9MfN9/1IANQrjYNC2/H+lYozrdKMGLJfx9TUVEURVEURXmzcnsUi/ZSyuJSSkMppaOU8uen67dIKd+TUpaTUk58jeNulFL2trCwyPmg/4tSfhR2ssLlym8YJcVRLPQBS/aczXo/RVEURVEUJd8okIUw+bYH2cAIUXskTporuIf9QuEnRTi+aT8nrsTmdWSKoiiKoihKNhXIBDnf9iADuDbH3EkPG9OblLy5nQpxTkyZvZHgszfzOjJFURRFURQlGwpkgpyvGRdGuDTBzu0OFU5vIkX/FNViHZky/wCzgyPJ7XGnFUVR8ht9fX3dRCHu7u5MmTJFN81scHAwFhYW6Z5437Hjxec3nJyccHNzQ6PREBQUlO0Zu3Lb9OnTc3zCiT///FN3LczNzXF2dsbDw4OPPvooR9t5nqOjI/fu3Xth/YoVK6hUqRJ16tR57WNfvXqVNm3aUL58eby9vWnUqBGRkZFERkZiamqa7v4/m6kvLi6OXr16odFo8Pb2JjAwkCNHjnDp0iUCAgJwcXHB1dWVH3/8UdfOnTt3qF27NhUqVKBevXq6MXDDw8Px8/PD2NiY6dOnvxBfcnIyGo1GN6X0q0h7fZ4/n08//VS3XWpqKoGBgdmeNTGtESNGvDTuTp06sW7dulc+XkaCg4NxdXXFw8Mjw1n/Xkft2rVfGI84vyuQCXK+LbF4xr0d5tZ3KezjTO2DS7lhfpagB+as3nie/1t1UiXJiqK8U0xNTQkNDSU8PJzt27ezdetWxo4dq/t9zZo1dVP5hoaGZpiI7dq1i5MnT+Lj48PXX3+d7fafTU2bG14nQc4qnnr16umuhY+PD8uWLSM0NDRXh8rKzIIFC1i4cOFLP7i8THJycrplKSXNmzcnKCiIyMhIjh07xoQJE7hx4wagnVY67f1/NuFD9+7dsbe358SJE7pJNm7fvo2hoSHTp0/n9OnT/PPPP0ybNk03pffEiRNp0KAB58+fp2bNmnz33XcAWFtbM3PmTAYNGvTSmKdOnUrlypVz5PqkPZ+0U3xv3LgRHx+ffD1h0dKlSxk5ciShoaFZzpD4Kjp06FDgRrcokAlyvi6xACjjD+Z22NUwQj/pMQHxu7ld6AofJhhy5NBVftpzIa8jVBTlHbR35TnWTjmeo6+9K8+9Ugy2trbMmzePH3/88bU7Cz744AMiIyMB6Nu3L/7+/ri6uqabvtrJyYkvvvgCLy8vVq1axfz586lSpQru7u60bNlSl9R27dqVvn374uvrS9myZQkODqZ79+5UqlSJrl276o73119/4efnh5eXF61bt+bBgwfMmDGDq1evEhgYSGBgYIbbvSyeGTNm4OLigkajoV27dtk+9wULFjBw4EDdcv369dm3bx/JyclYWloybNgw3N3d8fPz4+ZNbWnfjRs3aNGiBT4+PlStWpWDBw8CcOvWLerWrYurqysff/zxS+/HqFGjOHjwIF26dGHYsGEkJCTQpUsX3Nzc8PLyYs+ePbq4mjdvTmBgIPXq1Ut3jO3bt2Nubk7Pnj116zw9PTOdGvzs2bOEhoYyduxYxNOZacuVK0eDBg1wcHDQTZNcpEgRKlasSEyMdjqF9evX06VLFwC6dOmi6121s7PDx8cHA4MXR7e9dOkS27dvp1u3bpld+pd6/vpkZtmyZTRr1ky33KRJE7y9vXF1dWXBggW69Zs3b8bLywt3d3eCgoJeOM6cOXNo1KgRiYmJ6dYfOXIEf39/vL29adCgATdu3ODs2bNUqVJFt01ERARVq1Z9aXxz585lzZo1fPnll3z00Ufs2LEjXY96nz59WLp0KaD9tmHMmDF4enqi0Wh0H1Di4+Pp0qULGo0GjUaju/7NmjXjt99+y/T65DcFMkHO9/T0oVwtjOIOUbRDe4rvPUWM5w7iTG/ROsGYFRvPqZpkRVHeWWXLliUlJUWXwO3duzfdV+z//vtvpvtv2rQJNzc3QNtjuHv3bk6ePKn785lixYpx/Phx2rVrR4sWLThy5AgnTpygUqVK/Pzzz7rtYmNjdT2RTZs2ZdCgQYSHh3Pq1ClCQ0O5ffs2EyZMYMeOHRw/fhwfHx+mTp3KgAEDcHBwYNeuXezatSvD7V4Wz6RJkwgJCeHkyZM51rN2//59/P39OXHiBH5+fvzyyy8ADBgwgKFDh3L06FFWrlypS1RHjx5NYGAg4eHhNGzYkKtXr75wzHHjxuHh4cHvv//OpEmTmDFjBsbGxpw6dYpff/2Vzp07676KDwkJYc2aNfz999/pjhEWFoa3t3eGcZ89ezbd/T9w4ADh4eF4enpmOanGhQsXCAsL0yWBd+7cwcbGBoASJUpw7dq1LK/bwIED+f7773WJ+Kt4/vqAdnpjT09PAgICOHDggG7bAwcO4OXlpVtevHgxx44d48iRI0ydOpXY2FiuX79O3759Wbt2LSdOnGDFihXp2ps+fTp//fUXa9euxcTERLf+8ePHfPbZZ/zxxx8cO3aMTp06MXLkSJydnTE1NSUsLAyAhQsXZvhBoE+fPjRs2JBp06Zl69sKOzs7QkJC6Nmzp+59PmbMGGxsbDh58iQnTpzA398f0Pbgx8fHv7SEJ7/KN1NNv3XKBsKJ5RT2c+Lu4lSGGATQxXUWbc59Tov7Fny/MJTS/1eDMtaF8jpSRVHeETXbvJfXIbxUzZo12bRpU5bbBQYGoq+vj0ajYcKECQCsXLmSuXPnkpqayrVr1zh9+jQajQaAtm3b6vYNCwtjxIgR3Lt3jwcPHqTr5WzSpAlCCNzc3LCzs9Ml366urkRFRREdHc3p06d1PZ5JSUn4+fm9EN/Bgwcz3S5tPBqNho4dO9K8efPXqnt9GVNTUxo0aACAt7c3e/fuBWDHjh2cPfu/IUdjY2NJSEhgz549bNmyBdD28GVnyt99+/YxZMgQQHt9HBwcdL35QUFBFC1a9JXjflaSkNaaNWuy3C8uLo6WLVsyc+bM1y5bWLduHSVLlsyw9v1VOTo6cvnyZaysrDh8+DAtW7YkIiICc3Nz4uLiMDMz0207bdo0NmzYAEB0dDT//vsvV65cITAwkNKlSwNgZWWl237hwoWULl2aNWvWvNATHhERQXh4uK48KSUlBUdHR0A7e9/ChQv59ttvWbVqFSEhIf/5PAFatGgBaN9rz95HO3bs0PUaCyHSvR9sbGy4du0alpaWOdJ+biuQCbIQognQpHz58nkdSsbKBYK+EaYhX2FUyonkKfMZ8GVjppafRM/LE6l3U5+BMw8ypJM771ewzutoFUVR3pgLFy6gr6+Pra0tERER2d5v165dWFv/79/LixcvMnnyZHbu3EmpUqXo2rVruq+dCxX6XwdE165dWbduHe7u7ixatIjg4GDd74yNjQHQ09PT/fxsOTk5GX19ferWrcvy5cszjU9Kmel2aePZvHkze/bsYePGjUycOJFTp0699Ov/5xkYGOgecATSnW/amlF9fX1dLbCUksOHD+doTenLpD2/tFxdXbP1Aej5fUJDQ9Oda1pJSUm0aNGCbt260bRpU936YsWKcevWLWxsbIiJiaF48eKZtnPgwAHWrFnDhg0bSExMJC4uji5durB48WLdNlFRUboPMf369UtXKvI8ExMTXc9u1apVKV26NJGRkXh4eKTrDd+xYwd79uzh4MGDmJqa8v77779QMvE8Nzc3QkJCiImJ0SXQz0gp0Wg0ug9FabVu3Zqvv/6aGjVq4Ofnl+0ENbP3Gvzv703a91pmEhMTMTU1zVbb+UGBLLHI9zXIAOa20HEVwsiUUtWvoWdijO/yU5S1LcXq977HzMoI/1uCfvMOM3JdGI+Ssn5zKYqiFHS3bt2iT58+9OvX77W+0k4rLi6OQoUKYWFhwY0bN9i6dWuG28bHx1O8eHGePHmiGyUhu3x9fdm/f7+up/Thw4e6msvChQsTHx+f5XZppaam6noKv/32W+7fv5/tkQ2cnJwICQlBSklUVBTHjh3Lcp86deqke1jsWW/tBx98oKsL3bhxo+48MlOzZk3d9YuIiODatWtk1VkVFBREXFycruQD4MSJE+zfvz/DfZydnXFzc2PcuHG62uiLFy+ydetWpJR07doVDw8PBgwYkG6/pk2b6pLbxYsXp6v5fZnvvvuO6OhooqKiWLp0KUFBQemSY9Be82cP3WWWHIP2/f3sIczIyEguXLhAmTJlAChfvjxRUVGAthzGysoKU1NTwsPDOXLkCADVq1dn165dXLp0CYC7d+/qju3j48Ps2bNp0qTJC6O4uLi4EBMTw+HDhwHtB4jwcO0kxWZmZtSqVYt+/fq9Up116dKlCQ8PJykpidjYWHbu3JnlPnXr1tW916SUxMZq54FISUnh9u3blCpVKtvt57UCmSAXGGUDoPlsDLmKTTNPHp8KY1xife7Im1yosYMipgZ0Ty3EygOXaD5rP7fiH+d1xIqiKDkuISFBN8xbnTp1CAoKSvdA3fM1yKtXr87Wcd3d3fH09MTb25sOHTpk+tDX+PHjqVatGjVq1KBixYqvFL+NjQ2LFi2iffv2aDQa/Pz8OHPmDAC9e/emfv36BAYGZrpdWikpKXTq1Ak3Nzc8PT0ZMGBAtnv1/P39KVGiBJUqVeLzzz/XPayWmVmzZrF//340Gg0uLi7Mnz8fgLFjx7Jjxw4qV67Mpk2bcHBwyPJY/fv3JyEhATc3Nzp27MiSJUuy7JkWQrB+/Xq2bNlCuXLlcHV1ZcSIEdjb2wMv1iA/S7AWLlzIlStXcHd3p3LlynTv3h1bW1t2797N8uXL2b59u26fP//8E4Dhw4ezefNmKlSowJ49e3TlINHR0Tg6OjJjxgzGjBmDo6Njjg/PB9pvOTQaDR4eHrRt25b58+fzrDOvUaNGum8uGjVqxKNHj3BxcWHEiBFUq1YN0Nb1zpkzh2bNmuHu7q4b0eMZf39/Jk2aRKNGjdIlz8bGxqxevZrBgwej0Wjw9PTk0KFDut937NgRQ0NDateune1zKVOmDM2bN8fV1ZV27dqlq5/OyOjRo7lx4waVK1fGw8ND16N95MgR3n///SxryvMTUZCHHPPx8ZFHjx594+0GBwcTEBCQvY2lhCXNkFH/cPFIFVIeJLBnbGOmRy3i67LTiV6hh6mDGVMSYrGzMmV5L19sChtnfVwlV7zSvVUKlHf13kZERFCpUqW8DiNXxcfHZ6t+VimY3pb7Gx0dTc+ePdm2bdsbb3vSpEk8fvw43QfTN+nTTz+lTZs2uof2nsnte/uyf/+EEMeklD5Z7VtwUvk08v04yGkJAS3mI4xMKP5+Min37lFz1AbqxpViytXx+LYvzaPohww2tOTunQTazz+oepIVRVEU5S3j6OhI165dX2uikP+iSZMmrFixgv79+7/RdtPy9PR8ITnO7wpkglwgapDTKmwHtUdhmngIp0G10DMyptdPlyl8JZbf5Twa9HEj6XYin6SY8+B2Ah0XHOT+oyd5HbWiKIqiKDmoXbt2b3yikI0bNxIaGppuRIymTZumK2vJqVE8MpJV7XZ+VCAT5AKpje43gAAAIABJREFUSk9wboTJ1ZU4zZuC0Ndn2GF7/jy3iYtFT9FskCciWdIt0ZT46wn0+vUoj5Nzb/YnRVHeHQW5lE5RlJy3YcOGdLMXZjaDZUH1X//dUwnymyIEBI2HJwkYRCzB9vPPsTp5mRFbzZiwbyx69o9pOcQbY0N9PnpsysXIWD5feYLUVPUfm6Ior8/ExIQ7d+6oJFlRlHeGlJI7d+6km0zlVRXIcZALrGLloEJdODwPq547gS/h629obJ7IIMtBLKy3kOaDvVg3LYSuj8xYFHKdby3P8GXDt/sBG0VRco+joyPR0dHcunUrr0PJNYmJif/pP0Ilf1P39+2Vm/fWxMREN1nK6yiQCXKBmCgkI42mQuTfsH0kVh+t5/H5SOqvXk2h+BAWXOlH394/8eHnnqybGsJHD0z5dedFHCxN6VLdKa8jVxSlADI0NNSNw/q2Cg4OxtPTM6/DUHKJur9vr/x8bwtkiUWBe0gvrSLFocG3cGk//NEDu6FDsGzalBrn9Aictped4/piYWNG88FeFClsRMdEE+asPc22sOtZH1tRFEVRFEX5zwpkglzg+XSD9wfD6fXo/VoPh48b4HzgAOeq2OOwfDcH5k/EwsaUDwd7YVnEmHYPTZj4ayjHLsXmdeSKoiiKoihvPZUg55Xao6Dpj5B4H5a1xvDJHerMW09UeXNMZi7l8Plgilg/TZItjGkZb8SXPx1h3/nbeR25oiiKoijKW00lyHlFCPDqDD3+An0jWNKUQklx+Ez4EdMk2P3NZ4TfDqeItSkth3hjaWVCo1gDRs47yi/7Lqon0hVFURRFUXKJSpDzmkUJ6LIBHt2FRY2wST6NUeMgGhxMYtG07tx6dIvCVia0GeqDTfFCtHhgxNK1Z/hyzSk1BJyiKIqiKEouUAlyflDCCzquAuMisGkgTlZ/YeBUnI/WxjFtcR+epDzBrIgRLT73wq5kYVokmrD9YDST/zqb15EriqIoiqK8dQpkgiyEaCKEmHf//v28DiXnlK4OH++BzmvRNzehvN8VKGJG5x9OE1I/gCfXr2NSyJBGn2gwK2TIRymFWLDrX1Yfi87ryBVFURRFUd4qBTJBLtDDvGVGCChXC1rMx8A4Fed68YQ0dsDgxl0ienUhNSmJQpbG1O9VGcPEVDoLc7784ySHL97N68gVRVEURVHeGgUyQX7rOfpAl40Y2dvQsvBR1jYvjOH5y5zv0YXUR49wqGDJ+63LY3UvhfrShN5LjhJxLS6vo1YURVEURXkrqAQ5v7Jzgd7BGJeoQh+jSLb6ClKPhHKxc2uS79zBLcARl5oOVIwF34f6dJx/iDPXVZKsKIqiKIryX2WZIAsh9IQQnkKIRkKIWkII2zcRmALoG0KP7ZRosZAP6pTlxyaCR2f+5d9a/jzYuZOA9s641HTALU5QLV6PDvMOcfZ6fF5HrSiKoiiKUqBlmCALIcoJIeYBkcAkoD3wCbBDCHFQCNFNCKF6oHObEODSDJ9OmwjoOpzhHxnw0OgJV/9vEInHDxPQ3pnK/iWoHC+oEadHh3kHuX4/Ma+jVhRFURRFKbAyS3AnAEuBclLKelLKTlLKVlJKDdAUsAA6v4kgFa3Wrp3wrtGcIZ0MSdZL5OonXUjdOZUPmtniXqckFR8I3r8Dg1eEkKLGSFYURVEURXktGSbIUsr2Uso98iVTtkkpb0opp0spF+dueMrzhlYbjqGjIz+1siEpTnB55GxY2JAaQeb4Ni/Le4/1sT/1gJ92RuZ1qIqiKIqiKAVSdmqQzYQQI4UQ858uVxBCNM790JSXMTM0Y+L7Ewl2iCe0gzeJsUbE7ruEmFoR7ztD8a/9hLLJ+kSvu8Sxc7fzOlxFURRFUZQCJzs1xAuBx4Df0+UYtOUXSh7xtvPmI5ePmFQylCQ/d26csOS+cUuIPkLl8DYEOSyieDJs+/Ek4RfUGMmKoiiKoiivIjsJcjkp5XfAEwAp5SNA5HQgQoiaQoi5QogFQogDOX38t01/r/6UtarAZ/5R4O7C1cX/cKfYcGjzK+8VPU3zomOxfPKEdZNDmPVHOE9SUvM6ZEVRFEVRlAIhOwlykhDCFJCgHd0CbY9yloQQvwghbgohwp5bX18IcVYIESmEGAYgpdwrpewDbAJUbXMWjPWNmVNnDobmhRlc7wZ6nm7cnDqdm9v+JaXjFkq6OdDSejRF9Z7A9huMG7abyPOxeR22oiiKoihKvpedBHk0sA0oKYRYBvwNDM3m8RcB9dOuEELoA7OABoAL0F4I4ZJmkw7Ab9k8/jvNvpA9P9X9iYcmgmGNYjH8oDp35s/nSr9BJNcYi30ZS3padcHLcgO2D56wbUoIJ/fH5HXYiqIoiqIo+Zp4ySAVL24kRDHAF21pxUEpZbaf/hJCOAGbpJSVny77AWOklPWeLn8JIKX8RghRChgppeyVyfF6A70B7OzsvFesWJHdUHLMgwcPMDc3f+PtZuTy48vMuDEDGwMbvrzyPtaLf0MaGvKoTm3MvC1wvL4Jo3tXWRY7gYQnJSntLyhSXA1h/TL57d4qOUfd27eXurdvN3V/3155cW8DAwOPSSl9strOIKNfCCG8nlt17emfpYQQpaSUx18zthLAlTTL0UC1pz/3QPtQYIaklPOAeQA+Pj4yICDgNcN4fcHBweRFu5kpF1OOT//+lBXeMUxuvZ67M2aht3kLptHumH6zHKPzv9B5/5csvTOFy/vsaDvMG5uShfM67HwnP95bJWeoe/v2Uvf27abu79srP9/bzLoRp2TympwbwUgpR0sps3xATwjRRAgx7/79+7kRRoFUo0QNvvL9ir0xe5l6cwUlpk3FbsQIEk6e5EKzD7l9yQmjhuNpWWws5vIO678LJvrA0bwOW1EURVEUJd/JsAdZShmYS23GACXTLDs+XZdtUsqNwEYfH58MSzHeRa3fa82V+CssDFtIWYuydOjUkULV/bg+bjy3pk7lSevW2A7YjPmcGTy64cf6JXGU2b2R6t0CsbRXX18piqIoiqJA9h7SQwhRWQjRRgjx0bPXf2jzCFBBCFFGCGEEtAM2vMoBVA9yxgZ6DcTf0Z/JRydz+s5pjMuWpdTCX7Bs04Z7a9ciDYrxXvdxzCmcQkWrHVy5rMeKsQcI2/AP2alHVxRFURRFedtlZya90cDMp69A4DugaXYOLoRYDvwDOAshooUQPaSUyUA/4E8gAlgppQx/laCllBullL0tLCxeZbd3gp7QY0KNCViZWDFk9xAeJD1ACEHRjh0gJYVb06ajcbSkVjV3esnq1G0UQwnjcHZvSWD7iNkkHV8DqWrMZEVRFEVR3l3Z6UFuBdQGrkspuwHuQLYyUylleyllcSmloZTSUUr589P1W6SU70kpy0kpJ7529MpLWZpY8t0H3xHzIIZx/4xDSomJszOWbdpwf906kmNjGVLPGUszY4ZeqUzDid2oprlK5B1nfv85kUMzl3DzUpzqUVYURVEU5Z2UnQQ5QUqZCiQLIYoAN0lfQ/zGqRKLrHnZefGpx6dsjdrKzJCZSCkp2r4dMjWVqLbtML0ew7AGFTl6KZbfTt7D55NONP3Mg0KF9TgW4ciqb46ybOQ+ok5le0Q/RVEURVGUt0J2EuSjQghLYD5wDDiOtmwiz6gSi+zp4daDlhVaMv/UfIbtHYZehbKUXrKE1IcPudS+PfXvn+OD92z4essZLt5+iGMla1qMrEu3St9Qq8hM9GIvsnnWSbZO2Un87Ud5fTqKoiiKoihvRJYJspTyEynlPSnlXKAu0OVpqYWSz+kJPUb7jeYzr8/YcnELvf7qRUKlUjj9tgyDEg7EfDaQ0WfW4hwXw6DfQ0lOSYVCxTDt/xeVBo2lbYtr+BZdw+Xzj1k5Npi7UTfy+pQURVEURVFyXXYe0vtQCGEBIKWMAi4LIZrndmBZxKRKLLJJCEFPt558/8H3hN0Oo9XGVhw3ukbpxYuxaNyY5L+38/WOaXhtXcrSZTu0O+npQ3EN+rW/wHvsd7SpcxyRksDGyfuID9mZtyekKIqiKIqSy7JTYjFaSqnLRKWU94DRuRdS1lSJxaurX6Y+vzX6jSJGRej9V29mnV+I3dcTKPfXn5g4v0fryGCqff0Zp/oMQKak/G9HIzOKtvqKJm0MSUoxZuOCaBJ+bgtHfoYnCXl3QoqiKIqiKLkkOwnyy7bJcIIRJf9ytnJmeaPlfFjhQ+afms+kw5PQt7Ki7Jo12O/ay5/u9TAI3s65keOQT56k29cmoCmNPqtKHA6sCW1G1B/LkD9WhYt78uhsFEVRFEVRckd2H9KbKoQo9/Q1De3DenlGlVi8PjNDM8ZWH0s3126sOLuCn07+BEDR4ta0mP012yoGkLpmJZEdOpEcG5tuX4eKtjTu74UsWobN90awPupjLs8fTdIfn0FC7MuaUxRFURRFKXCykyD3B5KA35++EoFPczOorKgSi/9ukPcgmpZryqzQWaw6twqAUsXMqPPjJKb6dibh9GkuduzEk+vX0+3nWNGK9qOrUbPte9wx0LDx7kgWbG/Mqq+2EDrvV1LOqRplRVEURVEKtixLJaSUD4FhAEKIosA9qWaQKPCEEIypPoZ7j+8x/p/xJKUk0bFSR9wcLej4ZU9GTzFn5D8Lifj0MzR//J5uX319PTSBjlSqXpxrkfe4unsn0WcT2H+8LGEnrlKjwkCcmrVClK4OetmazVxRFEVRFCXfyDB7EUKMEkJUfPqzsRBiJxAJ3BBC1HlTASq5x1DPkMn+kwksGcikw5P49vC3pKSmEOBsy/jRXdhUpRmG4SfZ3PlTHt58ccIQQ2N9SrkWw/eT1rT6oSeNe5dFz8ySLWeasnX6XhLGO8PSlnBsETyOf/MnqCiKoiiK8hoy695rC5x9+nOXp9vaAv7A17kcV6ZUDXLOMTUwZWrAVDpV6sTSiKUMDh7M45THVC5hwZDZw/m3Wh3KHtnJ6fqNuHfhUqbHKu3lRNtJDaneyJZLT6qy4u4PXLmYDBs/g5nesH00xF19Q2emKIqiKIryejJLkJPSlFLUA5ZLKVOklBHk8SgWqgY5Z+nr6fNF1S8YVnUYu67sov/f/UlITsDUxIhGi2Zw5avv4UkSoZ26cW3TNjKrsNHX18OzSWVaD6+KsVUxNsR8xk7bDcQbO8P+6TC1Euz65g2enaIoiqIoyqvJLEF+LISoLISwAQKBv9L8zix3w1LyQsdKHRlfYzyHrh/ikx2f8OjJI4QQBHVuTMKwcegnPOLe/w3i7KjxyNTUTI9l7ViY1l/64FGnJGdPp7Ls7Ofsc9rK4+L+sHsSzPKFg3Ph7gVQJe2KoiiKouQjmSXInwGrgTPANCnlRQAhREMg5A3EpuSBZuWb8c373xByM4Q+O/rwOOUxAB90bIL+ivX8XcYXuWo5qzr241RoZKbHMjTSp0arCnQc60uFqnacPJTIiotDueI6GQxNYdsXMMMT5vlD2B8qUVYURVEUJV/IMEGWUh6SUlaUUhaTUo5Ps36LlLL9mwlPyQsNyzZk0geTCLkZwoSDE3QlFdWd7Wi/ai6Xa9TDLWQXSZ1asW7271kcDYoUM6X2R5Vo+YUPBsYGbPi7HHstf+JJr0NQfxI8vA2ru8NUF1jWGkKWqln6FEVRFEXJM680BpcQYlNuBfIq1EN6ua++U30+1nzMush1rDy7Ure+mLkxQQumYbd6DfHF7HGeMYYzi5Zn65h2TkVo81UV3AIdObkzmuWz7xJVqDUMPAX+X4CjD9wIh/Wfwg/usKgxhK/NrVNUFEVRFEV5qVcdpLZErkTxitRDem/GJx6f4O/oz6TDkzh+47huvRACq8qVcFn9O6fsnUn+bgI35i0gNSkpy2MaGunzQdv3+PBzTwwM9dg86yRb55/mgcdgaPsrDAyDTmugZFW4dxn+6Akhy+B+tCrBUBRFURTljXjVBFnVHr9D9IQeX9f8mhKFSzBg1wBCb4am+72tjQUW303hRLHy3J06hSs9e70wPXVGHCoUpe2Iqvg2L8vlsDv8NuYQoTsukyqB8rWh7VLoHQyWpWD9JzDNFX6qCQfnqKHiFEVRFEXJVa+UIEspu+dWIEr+VMSoCHPqzMHCyIKef/Xk78t/p/t9varlON5/HFO82/Hw2DEuNGlK7KpVpD56lOWx9Q308K7vRPvR1She3pL9qyNZ+c1Ros8+TbLNrKDvAej+J9QZC6mpsG0YzPCCLUMh5UlunLKiKIqiKO+4LBNkIcQpIcTJ5157hRDThBDF3kSQSt4qWbgkvzb8FeeizgzaNYjF4YtJlf8b5m10UxfOutdkVIP/Q+obcH3kKK588impiYnZOn4Ra1Ma99NQv3dlHj98wvppIWyadYI7Vx9oR7so5QvvD4S++6HvP1CxIRz+Cf7dmVunrCiKoijKOyw7Pchbgc1Ax6evjcBR4DqwKNciU/IVKxMrFtRbQGDJQCYfnUy3bd2Iuh8FQBETQ+Z19iHc1J6vWo2h2IgRPDp0iKtDhiJTUrJ1fCEE5bxs6TjOF78Py3Et8j6/jz/Mrl8jeHj/8bONwM4Fms0CfSPYPgrObFa1yYqiKIqi5KjsJMh1pJRfSilPPX19BfhLKb8FnHI3PCU/MTUwZXrgdMbXGM/5e+dpuaGlboSLSsWLMKWNO0djHjDFxA3boUOI376dSx06knj6dLbbMDDUx6teaTqP98Mt0JEzB6+zdOQ/HNp4gaTEZO1GhqZQdzw8uAErOminso4+BqnZS8YVRVEURVEyk50EWV8IUfXZghCiCqD/dDE5V6JS8i0hBM3LN2d9s/VUKV6F8QfHsy1qGwAN3YrTL7A8vx+9wtKS72M3ciSPo6K43L0HiWfPvVI7JuaG1GzzHh3GVMPJzZqjm6NYOuogYXtiSE1JBd8+MPgMlK8DxxfDglracZR3ToSo/fDglkqYFUVRFEV5LdlJkHsCPwshLgohooCfgZ5CiELAN7kZXEbUOMh5z8bMhh8Cf8DL1ouv9n5FyE3tACeD677Hh54lmLbjHN8YuVJiya9IKYlq1467y5a9cjsWNmbU61WZll94Y2lryu7fzrJiwhGunr8HhibQ6Q8YehE+/Ans3WDPd7CoIUwuD+Ot4fvysPn/4OGdnL4EiqIoiqK8pbJMkKWUR6SUboAH4C6l1Dxd91BKuTKr/XODGgc5fzDWN+aHwB8obl6cATsHcOH+BfT0BFPbuDOgdgVWHYum5+7bWK/8A1ONhhvjJ3C+Vi3ig4NfuS37MhZ8+LkXDfq4kfw4hbVTjrPr1wgSHz7Rjnbh3g46rYbPz0LbZdBwMtT8PyhZDY7MhxkesOZjOPG7mqVPURRFUZRMGWS1gRDCAhgNfPB0eTcwTkqpum8VLE0smVN7Dh23dKT1hta0cW5DD7ceDK77HmWszfhi9SkaLH3IxMHj8Q7fy73fVxL9yaeU27IZIyenV2pLCEFZDxtKVrLiyOaLhO64wsWTt6nRqgLvVbVDCAGF7aFS4/Q7XjsJB2fD2a1wcoW2ZtnpfajYSDvOcilfMCqUcxdFURRFUZQCLTslFr8A8UCbp684YGFuBqUULCWLlOT3xr/TuFxjlp9ZTsM1DZkTOoeGGhvWfFIdq0JG9Fp+kjGiEkWmzoDUVGIGf86TmJjXas/QWJ/qLcrTZrgPRaxN2bHwNBt+COXezQzGXi6ugQ/nwpBI6LASvDrDzdOwaSAsbQHTKsOyNrB/BoXjzqoeZkVRFEV5xwmZxRBZQohQKaVHVuvygo+Pjzx69Ogbbzc4OJiAgIA33m5BcCnuEjNDZvJn1J84FXFipO9IPGx8mLv7X2buPE8JS1PmR60jOVg74UjhunWxaNIYk8puGNrZvnJ7qamS8D0xHFz3LykpkqqNy+BRpyR6+ll89ktNhfuX4UY4nF4PMcfgTuTTXwqwcYZKTcH1Q+3QckqBp/7evr3UvX27qfv79sqLeyuEOCal9MlquyxLLIAEIcT7Usp9Tw9cA1BdbMpLlS5Smsn+k2lRvgXjD46nx189aOfcjqEBQ6lR3prui47Q0aEJS5b3p+i2tdz74w/it2lHwbBs3Rr7cWO1pRLZpKcncAtwpKyHDbuXn+Wftf9y/ugNAjpWxM6pSGY7QlEn7atiI+262EuE7fiNytYSLh2AvZO1D/0Vq6BNku01YFYMipSAYuW05Rn6hq9/sRRFURRFyZeykyD3AZY8rUUGiAW65F5IytugeonqrG22lhkhM/j19K9E3I1giv8UVvXxo/PPh2iz5l9+6doD74Gf8Sg0lPhtfxL722+YumuwbNXqldsrZGlMw74a/j1+kz0rzrF60lHKethQtUkZipUwz95Bipbmto0fPPs0++AmRGyAc3/BlcPanua09AygfF3waA8WJaG4O+jpv3BYRVEURVEKliwTZCnlCcBdCFHk6XKcEKIlcDK3g1MKNhMDE4ZWGYrGRsOo/aNos6kNI3xHsOrj6nRZeIQOCw7xfSsNzapWxaxKFR4dOcy1ESMxsLXF/IMPXqvNcl62lKxkxYmdVwjdfpkLJ25Rya841VuUx8T8FXt7zW2hSk/tC7S1yY/uwP1ouPOvto45dBmc26r9vaEZWL8HpatrH/wrVR3MbV7rPBRFURRFyTvZ6UEGtIlxmsVpwB85H47yNqrvVJ8KlhUYsmcIg4MH42XrxYS2n/HDFmM+WxHKv7ceMqhOBUpMncqlrt240vtjTD08KNazB+a1a79SyQWAkakBVRqVwc3fkWN/XuLk31eIOvXcaBevw9AULBy1r1K+2nWBw+H2OW3CHHMMLu6Fo79oR80QelDcAyq3AM/OYGr5eu0qiqIoivJGZTtBfs5rZhiZHFAIPWA8UAQ4KqVcnNNtKHmnnGU5VjZeydrItfwY8iN9dnahtVtbHIrWZcbf5wm9co+vGlai7No13F+zlvvr1xPdrz9GZctSuFYgZlWqYO7v/0ptmpgbUqNleZyr2RO87Aw7Fp4m5mwsgZ0qIvRy6C1sVAgcPLUvt6elISnJEH0Ezm7WJsx/jYDto8GqLNhXBmtnsK6gfQBQlWQoiqIoSr6TnWHeXibzoS+eEkL8IoS4KYQIe259fSHEWSFEpBBi2NPVzQBH4AkQ/ZpxKfmYgZ4Brd9rzZYWW+js0pnV51cSaTCe3nX1CLkcS4Mf9jB89zVSOnShzPp12I8di4G1NXcW/MyVj/tw/euvkSmvPn20taM5LYZ4492gNBEHrrHn93NkNXrLf6JvAKX9IGgC9A6GbluhxgDtyBgxx2H3JPijh3Z4ud3f514ciqIoiqK8lgx7kIUQp3h5IiwAu2wefxHwI7AkzXH1gVlAXbSJ8BEhxAbAGTggpfxJCLEa+DubbSgFTCHDQgytMpTAkoGM2DeC32OG0aJOa+TdIFYcusq+yNus/NiPkm3bULRtG1IfPuTayFHELvkVQzs7ivXo8cpt6ukJqjUtS2qKJOSvyxgY6lG9ZfnXL7fILiG0Ncmlq/9vXXISnFoJob/BrglwfAlYOWlHyyhWXtu7XKy8dpQM1cOsKIqiKG9cZiUWjTP5XbZIKfcIIZyeW10ViJRSXgAQQqxA23t8BUh6us2rdxMqBU4V+yr80fQPfjj+AyvPrcTC6C/6N/+YeZuL0XHBIVZ+7Ie9hQl6hQpRYuoUUu7f5868+Zj7+2NcvvwrtyeEwO/DciQnpRK64wpXImIpWakojpWsKFmxaNZjJ+cUAyPw7ATu7eHIzxB9GO5ehLDVkJhmgkp9I22SXMgWSlYBn+7aYekURVEURclVGU4UIoQQMovvobO5jROwSUpZ+elyK6C+lLLn0+XOQDVgKDATeASckVLOyuB4vYHeAHZ2dt4rVqzIrPlc8eDBA8zNszl0mJIt0UnRrLq7iguPL+Bs6E3Y6eYUNTbiy2qmFDHS9vLqX7+O1feTISWF+z16kORW+bXaklJy9zzER0se3QaZCiaWYO8tkKYP8+7eSonhkzjMHsVgmhCD2aMYTBJvYJJ4G/MHF5BCn7tWXiSYFueJYWESTIvzwLwsiSa22p5qJVPq7+3bS93bt5u6v2+vvLi3gYGB2ZooJLMEORjtSBXrpZSX06w3At5HOxbyLinlokwbyGaCLKXsl+VZPUfNpPd2kVLyc9jP/HD8B5zMK3L+VEvKFC3Bil6+WJhph2h7cv06l7t0JenSJezHjKZou3b/qc0nSSlEnbjNgTWRPIh9jEVpaPBR1eyPnfym3DoLu7+FK0cg/hqkPvnf70yLasdgfn8QlA3IqwjzPfX39u2l7u3bTd3ft1dBnUmvPtAdWC6EKAPcA0wAfeAvYLqUMuQ1YosBSqZZdny6LtuEEE2AJuVf42t2Jf8SQtDTrSdlLcoybO8wrN+by79ne9FloR5Le1bD3NgAQ3t7nFav4krvj7k+ZiyPjh+n+Jgx6JmZvVabhkb6VKhih5PGmmPbojj+5yVWjD+MTanCVPQrTlkPa8yLmuTwmb4GG2do9Yv2ZynhcRzcjoTrJ+BqKJzZDL9+qJ0VsHIr7Z9qlj9FURRFeS0ZJshSykRgNjBbCGEIWAMJUsp7/7HNI0CFp0l3DNAO6PAqB5BSbgQ2+vj49PqPsSj5UK1Stfi1wa90+7MbpV1WcOpUN7ovOsLiblUxNdJHv3BhSs6dw60ffiB2xe8kXYyi1C8/o1+48Gu3aWisj2+zcjwwuYyNYXnO/HONvb+fY+/v57AuaY6TxhonN2tsSxXOuSHiXpcQYGIBjt7aF0DAMNgzGSI2al9l/MGjg7Z32biI9k/r97RTbCuKoiiKkqlsjYMspXwCXHvVgwshlgMBgLUQIhoYLaX8WQjRD/gTbW/0L1LK8Fc8rupBfss5WzkzNWAqfbf3xd1rI0eOfEi/344z/yMf9PQE+hYW2I8aham3N1c//z9uTf8Buy+HIQxed2hvLQNjgXtASdxrleTu1YdEnbpN1MnbHNsSxdHNUZgVMaKIRGcqAAAgAElEQVR05WI4vGeJQ3lLChczyf2RMLKjiAM0ngoNvoUdY+CfWXBxd/ptipUH20raHmbX5nkSpqIoiqIUBP8tm8iClLJ9Buu3AFv+w3FVD/I7wLe4L1/5fsXYf8ZSo6oNfx+qyfy9F/jYv5xuG4tGjXh09Cixy5bxcN8+in/zDWZenjnSvpVDIawcCuFVrzQJD5K4HHaHiyfvcCH0FhEHtJ8Xi9qb8X7rCpRyLZYjbf5n+oZQbyIEfqWtVU6Ihcfx2mmxz2zW1jBHbITQeuD3CTjVVEPJKYqiKMpzcjVBzi2qB/nd0eq9VlyKu8Si8EWUqBTJ5J3NqVLGCq9SRXXb2I8aRaHq1bn57Xdc7tIFp99XYOLikqNxmJob4exbHGff4shUyZ2rD7kWeY+Tu6LZOPME5b1t8fuwXP7pUTYyg2L/+yBBuUDw+1Q7BvM/P8LO8XD+T/DoCPW/0ZZsKIqiKIoCZCNBFkIUQlt7nCqEeA+oCGx9WnaRJ1QP8rtlsPdgKhStwDeHvsGkzHQ+XneJHT2H6Ua2EEJQpG5dzLy8uNC0GRdbtMSsWjVKzp2Dnqlpjscj9ATWjuZYO5rjUsOBkO2XOLrlEpHHbmJgpEcRa1NsShamvI8tJV2s0H9T4ytnh4ER1BysTYx3joeQX7UTlliV1SbJlVuAXz81bJyiKIryTstOD/IeoKYQoija0SuOAG2BjrkZmKI8I4SgabmmVLOvxqCdwzklVvL/7J13eBzlubfvd7b31RbtqhdLtuXeKxB6Cw4hkIQeQgikkISck3DCSfKFnNSThJzQQggQCBBKaMEQihPAYGwMLjTbcrclS1Yvq63aNt8fI6vYli0by5Lt976uuWbmndnZZ7zW7m+f/b3Pc+ljDu67+HoKc/qqV+i9XsqeeZrOv/+d1j/ew/YLFpH3i59jmzdv2GLTGRRmnV/G2DlBdn7cSldrglBLnJ0ft7Lp3UbMNgOVswNMPrWAnKBt2OI4ZBwBWHQ7TLoYdr2nWTCaN8CSH8HKe8ARhLHngacMcsr6JgNKJBKJRHICMBSBLFRVjQkhvgL8UVXV3wghPhjuwA4YkLRYnJAEbAH++uk/cdEz17Az8lfOvNvM1+edwQ2fKsds0Hy0hmAQ/7e/jXnCBBp/8Utqv3wtzgsuwPvla4647aI/Tp+FKaf1VS/MpLPUbmhny3uNrH+7no+X1lE4PofK2QHyxrhwB6wjb8VQdJr1Ysxpe4KGDx+DHcugbavWBnsPs74CC78DOSUjE6tEIpFIJEeRIQlkIcR8tIzxV3rGRnRWj7RYnLgYFAOPLrqbL7xwKa1Fj/KHpQ4Wf1jPQ1+eQ5GnL5vsOPNMrLNm0XzbbYSeX0x0+XLKnn4KQ37+UYlTp1com+KjbIqPWFeS6hW7WfdmPW88shEAs81AsNxJcIyLYLm26PQjbMXQ6WHG1dqiqhBu1OotL/42rH5Aa4V9/u+g7BQtwyyRSCQSyXHKUATyTcAtwHOqqq4XQpQDbwxvWBLJ4LjNbu45626ueOkKxk79O7urr+Lie1bw12vnUJXn7D1P53aT97OfkXP55ey8/Aq2nn4G5qlTKLz9dgzBoyfwrE4jM88tZcbZJXQ0xWjcHqJxW4jG7SF2ftwGgMmmp2JGLmPnBAmOcaGMhlrLzjwgD77yKrRvh8e+CM9+FQxWTSQbrGD1QE4p+MdDcLIUzhKJRCI5LjioQFZV9U3gTQAhhAK0qqr67eEO7EBIi4VkjHsMfzjtD3zrtW9RMOEhOrZdyxf+9A5/vnoW88cMLLlmrqqi7Omn6HrpZdruu4/tF36W4vv+jGXKlKMas1AEnjwbnjwbExZqmexEJMXurZ1sXdPMpncbWb9sNwaTjkCZll2ump+H03fkJxoeMp5y+NpyqFsFax6E5o2QjkO0BRKhvvOcBVA4Cwpn9wjmfM3HLLv6SSQSieQYYihVLB4DvgZk0CboOYUQt6uq+tvhDm4wpMVCAlqd5HvOvIdvvvZNXOV/xlp/Pdc+tIqnvz6fifkDy5aZxozB/60bsZ9+GnXfvJGdV1yJ/9vfwnPllcNS6WKomO0Gyqf5KZ/mJ5lIU/NxG7u3dtK4PcSal3ay9uUaxi/IY+a5JSMvlPVGKF2oLf2JtUNzNTR+BHWrNRG94fm+44oeXIXgKtLW7hLImwJF88A2SupHSyQSiUTSj6FYLCaoqtolhLgCeBn4AbAGGDGBLJHsYVZwFveffT83/PsG7AX34uz+Otc/vIbnb1yIz27a53zLxImUPfcsDT/8ES23/Z6Ohx/B+/Wv4frMhejsI1tlwmjWUzk7QOXsAACRjgRrX6lh/fLdbFzRMHqE8t5YPfsK53ATtG6Grnpo2QShXRCq0yYAhp8ENQsGG1ScodVrLpgJ5aeByT5y9yGRSCQSSQ9DEcgGIYQB+Cxwl6qqKSGEOsxxSSRDZrJ/MveeeS/XvnothWMfp/r9y/naI2t47KvzMO5n4ps+J4eiP95NbPVqmv/vDzT9z89of/AhSh/72whEPzj2HDOnXDaOGeeWsPbVWta/Xc/GFQ2Mmxekck6A/Er36Kqx3B9HQFv2RzIGtSvg3Xu1zPOmlyGb0jLNBTOhZCEEJ0HVhdrEQYlEIpFIjjJD+fS5F9gJfAi8JYQoAbqGMyiJ5FCZ7J/Mr0/5Nd9947tMm/EyK1d+mlue/Zj/vXgy+kFEpHXWLEoefYTwkn+x+3vfY+vpZ+AeO5ZEXj7mcWOP8h0Mjj3HzCmXjmXGOcWsfbWWDct3U72iAaNZR9EEL0VVORSO9+Dyj7LM8mAYrVBxpraA1t1vx1uwcxlsfgVW3AHZtHascA7kjgdHnrYEJ0NgEhjMIxe/RCKRSI57hjJJ7w7gjn5DNUKI04YvpIMjJ+lJ9scZxWdw8+yb+d9V/8vsWXaeWf0pdnXEuPOy6QSc+xdUQgic55yNqfJ5Oh59hLbFL1D7pS/hv+k7uD//eYRuRCsaDmCPUJ5/0Rh2Vbez8+NWata1sW1tMwBOn5niCV6KJ3kpHJeDwTR6Yj8geiNUnqktZ/0UshnYskSzY+xcBpte0SYD0vPDlWLQRLNvnOZpLpqjNTVRRmk2XSKRSCTHHEOZpBcAfgnkq6p6nhBiAjAfeGC4gxsMOUlPMhhXTriSlngLf1n3F+bOi/LR2vP59B3LuOPS6Syo8A36OFN5GcH/9//YUlFJ3l/+QuOtPyX67rsEfnALhkDuUbyDg2Mw6Xon9qmqSkdjjLqNHeyqbmfju42se6seRS/Ir3BTMslL+TT/6PMtHwhFB+PO05Y9ZNIQ3g0NH2oTAZvW9U0GXP4HKD0Z5nwVxi+SQlkikUgkn5ihWCweAh4Eftizvxl4khEUyBLJgfjuzO/iNXv57erfMnlWmNZtl3PlA+/y3TPH8s3TKg5YYziTn8eYfy2h5fbbabv/ASJL36To7ruwLVhwFO9g6AjRVzpuymmFZFJZdm/rpHZdGzXr21n+9Fbe+cc2Zp9fxvRzikevZ/lg6PTgLtaWqkV945m0Vnbu37fC36/WfMyTLoHP3KllpiUSiUQiOQyG8mnpU1X170AWQFXVNFrJN4lk1HL1xKv57Sm/ZWtoA7bSP3HWFCO3/Wsz1zy0ivZo8oCPFUKQe9NNlD/3LIZAgNrrvkrzbbeRiUSOUvSHj86gUDTew8JLKrn8J3O56ufzKZ/m593F23n616vZtaGdVPI4+vPV6bXM8X/VwOcfgmlXwEdPDGyTLZFIJBLJITIUgRwVQnjpMQAKIeYBoQM/RCIZec4tO5d7z7qXtngrW3S/4jvnWVm5vY1Fd77NrvbYQR9vqqyk9OmnsJ9+Gm333c/WU08j9MILqNnsUYj+yOD0WTjnukmc97XJxEJJFt/xAfff9BZ//+Uq3n56C7Xr244PwazTw8SL4DN3aBnk5bfD+ucgkxrpyCQSiURyDDIUgfwfwGJgjBBiOfAw8K1hjeogCCEWCSH+HApJnS45MLODs3n4vIdRhMKT9bfwk8/rCSdSXP2X92gJdx/08Tq7naK77qL0iccxlpez+/s3s/W006m/+WYSmzYfhTs4MpRP83PF/8zj09+cwvSzizGYdKxbWs8Ld37IA/+xjOf/8D5rl9TQWhcmncygqsdwJceZ1wACnroGfj8B/vUTKZQlEolEckgMpYrFWiHEp4BxgAA2qao6op82cpKe5FCoyKng0fMf5RuvfYPffXgz1513M3980c01D77H49fPw2k+eBtky7RplD76CF2vLiHyxutE/v0aXYtfwHXhhXhvuB5TeflRuJNPhtGsp3Syj9LJ2mTFVDJDw5ZOaqvb2bWhnXee3cY7z24DQNEJTFY9gVInZVP9lE7xYXUeI57espPhBzWw8SWtZNzyP4B/HJA/0pFJJBKJ5BhhqFX45wClPefPEEKgqurDwxaVRHKECdqC/PXcv3LTGzdx38Zf8rnTruPv/67guodW8+erZ+K2Hlz8CaMR16ILcC26gFRzM6133knoxX8SW7WK4ocfxlhYcBTu5MhhMOoonuileKLW7jna2U3dpg4iHQmS8QzxSJK66g52ftwGArz5NvIq3ORXuCme6MFkPfgXixHD7IJpl8HUS+HOmfDefXg950PXOHAEQQw+UVMikUgkkqGUeXsEGAN8QN/kPBXNaiGRHDM4jA7uOfMefrT8R7yw436qZkziw80z+PSdUe65YhZTCt1DvpYhN5e8n/0M5/nns+uGr1F77bXkfPGLuL/weXQOxzDexfBhc5sYNzc4YExVVdrqI+z8qJXdW0NsWtnIuje1MnLFE7xUzPATKHfh8lkQB6gOMmIIAVO+CEt/yeTda2Hdz8FoB3cJXPB7KJ430hFKJBKJZBQylAzyLGCCekybEiUSDaPOyK9P/jVT/VP56/q/Yix4mHD6Jb7w2CnccvJVlBzi9Wzz51N03300/L8f0/zb3xJbtYrCe/6IOE4ylEIIfIUOfIWa6M9msjTXhNm6tplta5rZ+VEroNVm9hbY8RXa8RXZyS1x4iuyj45/h0/dDNOvZO3SxczIVaGzBtY8BP/4BnzxEQhMHOkIJRKJRDLKGIpAXgcEgYZhjkUiOSooQuGKqiv44rgv8lrtazzw0YNU65/lN+tfY5LuTBaevBCDbuj2AdvcOVS8+iptDzxA829/x67rb6Dw7rtQjMeIZ/cQUHQKwXIXwXIXCz9XQWtdhJZdYVrrIrTuCrP5vUbWvaX90OQOWBk/P8i4uUHsOSPYGloIcBXQ5aqC+adqY0Y7LPsd3LMAqj4DZ/0PeMpGLkaJRCKRjCqGIpB9wAYhxHtA77R/VVU/M2xRSSRHAb2i55zSczi75Gzerl/BD16/jQ3qM5z+5Ap+tOA/Obvk7EPKgHquvRY1m6Xltt+z8+JL8HzlWpznnINiOYa62B0CQhH4ix34i/ssJaqqEm5LULexg40rG1j5j+2s/Md2/MUOSid7KZnkw19sRxnphiWn/RAmXAjrnob37ofmarhxlfQmSyQSiQQYmkC+dbiDOFSEEIuARRUVFSMdiuQ4QAjByYULeePyeVxwz53UGV/ke29+jyn+Kfxi4S8odZUO+Tq+r34VY0kJjT+5lYYf3ELzb36L50tfwnv9V0eH3WCYEULg9FmYcJKFCSfl09kcY9vaZnZ+1Maql3ay6p87MZh15Fe4KZ/uZ/y84MiIZUWBvCna4i6Bf/4HvHgTLLxJZpIlEolEMqQ6yOerqvpm/wU4f7gDOxCqqr6gqur1LpdrJMOQHGcY9Tq+N2EqM/Q/I9FwMRtbd3DpPy/jtZrXDuk6zrPPpvKtNym690+Yxo2l5f/+j23nnEvTr35NYtNm1HR6mO5g9OHOtTLz3FIuvnkm1/7mJM6+biJj5wQJtcR545GNPPmLVdRuaBvZICdeBKUnw4dPwANnwbbXRzYeiUQikYw4QxHIZ+1n7LwjHYhEMhow6gT3XTWHy6suoWPLN4lHPdy09CZ+v/r3pA6h2YQwGLB/6lMU338/eb/6FcbCQjoef5wdF17Iphkzqf/e90k1NQ3jnYw+LA4jlbMCnHr5OC6/dS7n3TCZdCrLC3d8yBM/f4/XHtrA+0tqadrZdXQblVg9cM2LcOljIBR45CJ454/Q+DHIuckSiURyQjKoxUII8XXgG0C5EOKjfoccwPLhDkwiGSksRh0/vXASn5lWwH89E2CX8gQPrn+QN+ve4tYFP2F67vQhX0vodLgv+izuiz5Luq2NyBtvkKjeSMeTT9L14ouYJlTh/uxFuD9/yXHrVd4fQgjKp/spmeRl3Vv11Kxvo7a6nY0rGwFw+sxUzgpQPt2Pv8hxdErIVZwB31oDfz4VXr1FGzv9R3DK94f/uSUSiUQyqjiQB/kx4GXgV8AP+o2HVVVtH9aoJJJRwMySHF769un8z4sBHl+3hDp1MVe/fDUXV17MjdNvxGfxHdL19F4v7ksuASDnyisIv/IKXS+9TNMvf0nrvfdiP2khzgsWYVswH6HTDcctjTp0BoWpZxQx9YwiAGJdSWrWtbJldTNrl9Sy5pUaLE4jJRM9jJ+XR/5Y9/B6uU0OuGEZtG6Gl2+GVX+Bhd8F3VB7KkkkEonkeOBA7/qqqqo7hRDf3PuAEMIjRbLkRMCoV/j5ZyczLujkpy+U4y1aynNb/8FLO17iyqor+fKkL+MwHnpjEFNZGaavfx3v175GdPkKQs8+S2Tpm4SeX4ywWDBVVmKqqMAyfRruiy5C6E8MgWZ1GqlakE/Vgnzi4SS169uoWdfGjg9b2fhOI7mlTmacU0zxBC8G0zB9iTBaIX8azLoWnrsB/m8inPoDmPElbXKfRCKRSI57DpZBvgBYg9Y5r3/aRgXKhzEuiWRUcdW8Esb4bdzwiBW7fT4zpr7LfR/fx9Obn+a/5/0355Scc1iZTSEE9pMWYj9pIdnubsJL/kX844/o3ryFyFtvEXr2WToee5zAf92MZcaM47K28mBYHEbGzctj3Lw80skMG1c28v6SGl65dx2gdf5zB6wEy50UVOYQHOM6sqJ5yhe1eskr7tQqXHTWwJm3HrnrSyQSiWTUMqhAVlX1gp61rHkkkQALxvj423VzueqB93h/9QX84YtXcv+G3/H9N7/PkpIl/Pfc/z5k20V/FJMJ16ILcC26oHesa8kSmn72c2qv+TLCaMRx5pmYp0zGNn8BpooxJ4wVQ2/UMemUAiYszKN2fTutdRE6m2O0746y9tVa1rxcg6ITFI73MGaGn/Kpfsz2oTd72S9CQNUFMP7T8LfPw/uPQvmp2iKRSCSS45oDTdK7UVXVu3q2J6qquv7ohSWRjE6mFLp7RPK7/PCJTh776p9Y2vQMd79/N6saV3HLnFs4r+y8I+aTdZ59NrYFC4kuX0707WVElr5J10svaQeFQO/zYZ0/D/vJp2A7aSH6nJwj8ryjFUWnUDrFR+mUvi8iyUSaxm0hdlW3s+39Ft54pI03HtmI2WbA5jbh9JkpqvKQjBxmRQoh4KTvwuOXwsMXwlX/gDGnHaE7kkgkEslo5EAWi2uBu3q2HwFmDH84EsnoZ1KBi8e+Oo/L7lvJlx5cw9Nfu5xTC0/lx8t/zH8t+y9e2fkKP573Y/xW/xF5Pp3dhvOcs3GeczYAqaYmIq+/Trq1jWRNDdFlb9O1+AUQQssuz52HoaAAQ37+CTHhz2jWUzzRS/FELwsurqClNsyu6nbC7d1EO7tpq4+w48NWAJrffQd3wIrTZ8EdsBIoc+IrtKPTH8RbXLoQvrse/rcEdi6TAlkikUiOc4Y682fYaywJIU4FfgasB55QVXXpcD+nRHK4VOU5+euX53D5fSu58v53+fsN83n4vId5ZMMj3PXBXZzzzDmcUXwGF1VexLy8eSjiyE3uMgQC5Fx2We++ms2SWLeO8BtvEF32Nm333ddbv9c8ZQq2BfOxzV+Adc7s476bnxCC3BInuSXOAeOdTTH+/dy72ISdrtY4u7d2kkpkAK2SRrDcReWsXMbMyMVsG8SaYXaCfzwsuw085TDtCtmaWiKRSI5TDiSQ3UKIi9CaiTiFEJ/rf1BV1WcPdnEhxF/QJvo1q6o6qd/4ucDtgA64X1XVX6NN/IsAZqDuUG9EIjnaTC1yc/+XZvOlB9/jmgff4y/XzOaaSddwevHpPL7xcV7Y/gKv7HwFr9nLwoKFLMxfyMKChbhMR7YDpFAULFOmYJkyBb7zHdRUinRrK6F//IOuV16l7c/30fanezHk52OqqsI6Ywamygr0gQCGggJ0dvsRjWc04g5Y8Y0XnHrqZABUVSXS0U3Tji4ad4So+biNpX/bxFtPbCavwoU3344n30ZehRtPnq3vQovugBe/C89/U5u8d/qPYPwFUihLJBLJcYYYrGOVEOLBAzxOVVX12oNeXIhT0ETvw3sEshBCB2xG69BXB6wCLgM2qqqaFUIEgN+rqnrFwa4/a9YsdfXq1Qc77YizdOlSTj311KP+vJLh53Be239vaOLrf1uDxaDj++eM4/K5JegUQXemmzdq3+D1Xa+zYvcKQt0hDIqBUwpP4YLyCzil8BSMuuGvSpGNx+l69VUir71OYtMmUrW1A44bioowjRmDsawMY3ER1rlzMRQWHncVMw702qqqSkttmM2rmmjYGqK9IUq6W8swewvsVM7OpXJWAKfPAtksrP0rLL8dOnZAYDKc9VMoP02WgRsh5Hvy8Y18fY9fRuK1FUKsUVV11kHPG+6WrkKIUuDFfgJ5PnCrqqrn9OzfAqCq6q969o3AY6qqXjLI9a4HrgcIBAIzn3jiiWGNf39EIhHsJ0DW7UTkcF/b+kiWRzd0U92epcSp8PWpJoK2PrGUVbPUJGtYG13LmugawtkwDsXBKY5TOMlxEnbd0fv/JMJh9M3NKJ2d6Jua0NfvRl9Xh669HZHS2mmrQpD1eOieMIF0XpCMz0fG6yWbk4NqsRyTGdNDeW1VVSUVhfBuCNWoxNu0cYsXXCUCcw4YdCnyOpYxdvdDmDJdtOdMZ3f+2aQMLsKOSrJH4cuPREO+Jx/fyNf3+GUkXtvTTjtt1ArkS4BzVVW9rmf/KmAu8DpwDuAG7hmKB1lmkCVHmk/y2qqqyosfNfCTxetRhODR6+YwPujc57x0Ns07u9/hbxv/xvL65Zh0Js4rO4+LKy9mqn/qiPmEVVUluW0biQ0bSNbUkqiuJvrOO6jx+IDzhMWCIRjEWFKCsbQUQ3ER9gULMBQWjuqGJp/kte1qjbNldRNbVjXTVh8ZcExnEHgsHXiS7+PSN+LQteA0hfFOm45p/lXgyAerV2aXhxH5nnx8I1/f45fRnEEeNZ9mPZ7mg/qaAYQQi4BFFRUVwxuURHIICCFYNDWfqjwnV97/Ll+8dyUPXzuHqUXuAefpFT0nF57MyYUns7VjK49WP8pLO17iH1v/QbmrnLNKzuLkwpOZ5J2ETjl6FSiEEJgqKjD1+7tSVZVMezupXbtI1teTbmom3dREqqFBq6CxciVqIkETgE6HIRjEUFSEeeIEzOOrMBQWYCwsROfzHdMTBJ0+CzPPLWXmuaV0NsUItyXojqdJRFN0NsVo3+2mrt7Hpq5034OWZPG8/j65hr/j1Ldit6WxOxXsuR5s46ZjHH8a2PxSOEskEskoZCQEcj1Q1G+/sGdsyKiq+gLwwqxZs756JAOTSI4EFbl2nvrafC6/fyVX3P8uP//sJC6clr9fgViRU8GtC27l+7O/z6s7X+X5rc9z38f3ce9H9+I2uVmQv4CTCk5iYcFCPGbPUb8XIQR6rxe914tl2rR9jquqSnLHTuJr15CsqyNVV0+ypob2hx+BHrsGgDCbtdJzBfkYCwsxFBRiKCzsE9CuIztxcThxB6y4A9b9HkunMkTauwm1xGj+eAuNO/OpaconHtZDGGhEm4HxNhjFe5iVMBZjN3Z7loCnizx/DN+kKvRlc8DiAcOxaWeRSCSSY50hCWQhxAKgtP/5qqo+fJjPuQqoFEKUoQnjS4HLD+UCMoMsGe0Ueaw8dcMCrn9kNTc9+QGPrqzh1s9MZFLB/oWgzWDjc5Wf43OVnyPUHWLF7hUsq1vG8t3LeWnHSwgEY3PGMjs4m9nB2czNm4vNYNvvtY4mQghM5WWYygc23Mx2d2tZ5x7RnKqrI1VfR7Kunvj7H5ANhwecrzgcmmAuyMdYUIihuAjTmApMlRXoPJ5jJvusN+h6BXTJpL5mJulUhmhnkmhngkh7N5Gdm4k2pYiHFeJhPS2dFra1lMEm4G2wKO9iV9pwGtvwObrw+dLk5Jqwu/ToLDawuLU22FaPZt+w5IDNB3oz6IxSVEskEskn5KACWQjxCDAG+ADI9AyrwEEFshDiceBUwCeEqAN+oqrqA0KIG4FX0cq8/eVQu/TJDLLkWCDoMvPcNxby1Opd/PbVTSy6622+fXolN51ZeUDB5zK5OK/sPM4rO4+smmVD2waW1y9nVeMqntr8FI9WP4pe0TMrMIuF+QsZ6xlLhbsCv8U/aoSkYjLtY9foT6ari1Rd3QABnayvI7ljJ9G3l6MmEn3XcjgwBIPo84IYAkF0Hg96vx9TZSWGQC56vx/FNvJfFg6E3qDD5bfg8lu0gbnBfc6Jhrpp2tpGa/UWoq0GIl25tITy2dZkhSa0CvGAVWnHoWvBrtuKQ2nBa6ghz1CNU9ek6WJXEfjGgj23ZwlC4ey+fYPlqN23RCKRHKsMJYM8C5igHsZsPlVVLxtk/CXgpUO9nkRyrKFTBJfOKea8yXn8dPF6bn9tC9tbo/z2kimYDQf3FytCYZJvEpN8k7hh6g0kM0k+aP6AZfXLeKvuLW5bc1vvuW6Tm+m505kVmMXM4EzG54w/qh7mQ0HndKKbMAHzhAn7HFNVlXRzM91bt5LcupVk7S5SjY2kGxpIbKgm09EBmcyAxwirFb3fh97vR+/zY8jPx1iqTSLU+/2ahWOvx4w2bFCtKisAACAASURBVC4T5TPzKZ+ZP2A8GU/TVh+hszlOpCNBuDVAuLWIto5udnamycR6Hm9J4XXFcIbrcUXrMacbMSZXYVJDGJU/YxQxTPo0xtxCFIsdzC6wB8CZD46gNpnQEdT2LTkyCy2RSE5ohiKQ1wFBoGGYYxky0mIhOdZwWQzc9oWpVATs/OaVTexqj/Hnq2eS6zAf0nWMOiNz8uYwJ28O/znrP2mLt7GtcxvbQtvY0LaBNU1reGPXGwDYDXam5U7TBHNgJhN9EzEog3SJG0UIITAEAhgCAVi4cJ/jvQJ6y1bSrS2kW1rItLaSbmkl3dJC98aNRF5/HTWZHPC4ALAlGMQ8cSI6twud04XO5URxOnu2tX2d04nicqFzOEZFVQ6jRU9ehZu8Cvc+x9SsSntDlIatnTRsC9HRGKOxxUsyvu8Xj14aQa8kMSkJjCKCkTBmpRG7sh6HrhmHrgWHOYYj14HV50Zx5mmiOacEXIWaqLblgl6WsZNIJMcvQ3n39wEbhBDvAd17BlVV/cywRXUQpMVCciwihOAbp1ZQ7rNx05MfcNHdK7j/S7Ooytu3FNxQ8Vq8eC1e5uTN6R1rijaxpmkNa5rWsLppNX+o/wMAFr2FKf4pvYJ5in8KJp3pE9/X0WaAgB4ENZsl3dBA986dZNrbyYS62PbhB+Sl0yS3biOxrotMV9c+Jez2RrHZ0LlcKC4Xis2q7dtsKDYbitWGYrejOOzoHA4UuwOds2ftsKM4HNoyjA1XhCLwFtjxFtiZ9KlC7d5Vle6YVmEjGU/3LBm646medbrfeJruWJJoOEFTRzeJPRXsQkATKCKNXWnHoWvCqXsXt+453PrduHQNOOxpjE5Xj3UjoK0dQa0dt8kBJqe2b7RLMS2RSI45hiKQbx3uICSSE4lzJ+XxlNvKdQ+v4pJ7VnDn5dM5ffzgYu9QCdgCnF9+PueXnw9AW7yNtc1rNcHcuJo/fvBHVFQMioHJvsnMCs5iqn8q43LGkWvNHTU+5k+CUJSeqhkFvWMfFRZQuFe9TTWZJNOlieVMZ4hMV4hsVxeZUBeZUM9+SDuejUbJtLaRqqklE42QjcZQY7GDx2I0ojgc6Ox7RLMdnV0TzzqHHcXu6BPZDodmP3E6UfZkuO32Q3pNhBCYbQbMtkP/tSCZSBNp7ybcntCWtgTh9gIirWXUtMSojvRz2rWBSd+N3dCJQ9eMkzqc4h1y9E/j1tfjUFoRoud8xaBZOjzlmmi2+bTJhfYA5JSBM0/zTetG/y8cEonkxOCgAllV1TePRiCHgrRYSI51Jhe6eP6bJ3Hdw6u47q+r+f4547nhlHIU5ciLU6/Fy1klZ3FWyVkAhLpDvN/8fq9gfuDjB8iomj/XY/Yw3jOe8Z7xVHmqGO8ZT7GzGEUcn7V6hdGI3udD7/Md/OT9oGYyZCMRMuEI2XAXmXCYbCRCNhzWxiJhbax3WzvW3dKijYXDZA8mshUFncOBLicHXU4O+txcDMEA+twA+kBAm6gYCKD3+VCs+y8/N1SMZj2efD2e/P1PekzG03Q2xwi1xAm3J4i0aUK6q62c+vYEqUSfz1uvV3E5uslxxHBaIjgNbTij2/CEN2FNLkfE29Hme/dgsELuBK02tD0XHHmacHbkaaI6pwzMh/9ri0QikRwKQ6liMQ+4E6gCjGiVJ6Kqqo7YO5W0WEiOB4IuM3+/YT7ff/oj/veVjaze2c5tX5iK2zq8P0e7TC5OLTqVU4tOBSCWirGxfSPV7dVsbN/IxvaNPLzhYdJZremFRW9hXM44TTR7NdFc4a7AKFspI3S6Hu+yCyg46Pn7Y4DIjoS17HX/THa4i2woRLqjg0x7B92bNxNZtmy/2WvFatUy5/n56HxereZ0YRE6T07vFwGdy3XY3mqjRU9uiZPckn3f/lVVJR5O0dkUpaMxRkdTjM6mGM2NMbbXJchmi4HpAJjtBnwFNlw54LRGcZvaCWRWYQt/BKE6qF8D0RYGCGi9GYrnafWhLW5tIqFZW/uba2FzEoxWzd4RmASjdIKqRCI5NhjKu+RdaLWKn0KraHE1MHY4g5JIThSsRj13XTadOaUefv7PDXz6jre596qZg9ZLHpYYDFZmBGYwIzCjdyyVSbG1c2uvYN7YvpHF2xbzxKYnANALPeXucsZ7xjPGPYZyVznlrnIK7AWjtnLGaGWgyB4aqqqSjUS0roZNTaSbW3onLKZ21ZFqaiS+YT2Zltb9Pl5xONC53VimTcM0phxzVRWGwkKMZWWIw+zsJ4TA6jRidRrJr8wZcCybVYl2dtPVEqdtd4TWught9VG2fRwnEU0BDuB03IELCI5x4Sqx4Mgx4DBHsRs6sau7UTa/CB07NQEd74B4J/T88jERYEO/J3Tkg7sIiuaCr7LPE212aZ5ooxWsPm0tkUgk+2FIaQRVVbcKIXSqqmaAB4UQ7wO3DG9ogyMtFpLjCSEEX1pQyrQiN9/421ouu28lj3xlLtOK9q1acLQw6AxUeauo8lb1jmXVLLvCu6hur2ZT+yaq26tZsXsFi7ct7j3HqBgpcZX0CuZyVzlj3GMoc5WhV0a+IsTxghBCs104HIPWmgbIxmKkGhrIdHSQbm0l3dpGprOTTGcn6aYmIm+9RdcLL/Ser8/Lw1xVhW3ePPTBAHq/XyuVl5Mz6HMMBUURODxmHB4zBeMGXqs7nqajMUrDlhC7t3RQ83Er8XBqwDlCOHF4ryW31EmgykmgzIW/0IZejUG8g1XL32D2tEmQjELLRtj5tpaBfufuXhG9DwYbVJzRY+EIgLMQPGVapQ5n/v4fI5FIThiG8okVE0IYgQ+EEL9BK/c2ooZEabGQHI9MLXLz96/N57I/r+Sq+9/l4a/MYXrxJxMmRxJFKJQ4SyhxlnBu6bm946HuEDtCO9gR2sH20Ha2h7azvnU9S3YuQe35idysMzPWM5YJnglM8GpLubv8mCg7dyyjWK2Yxow54DmZSJTu6g0ktmwh9t4q4h9+SOT11weco/P5MJaUaEtxMZapU7DOnXvY2eb+mCx6gmUugmUupp9dDEAqmSHSb6JgpKObjoYojdtCbF3drN2bIvAW2gmUOelIlFChr8Kdb0WUfwrm3qBdPBmFWDt0d0GiS1snI9Adhs2vQnM1bF+qjffH7NIaruSUalU53EWQN1XzSOuPvcovEonk0BmKQL4KTRDfCHwXKAIuHs6gJJITlQK3hSeun8dl963kqgfe48Evz2Z2qWekwzogLpOLabnTmJY7bcB4Ip2gpquGzR2bqW6vZkPbBl7Y/kKvTcOkMzE2ZywTvBMY5xnHGNcYxrjH4DIdPXuJBHR2G9bZs7HOno3n8stRVZVMW5tm12hqIrltG93bt5OsqSG6bBmhlhYA9Lm5WKZNw5CXh6GkGPfnPodiPrS63oNhMOrICdrICe47WTAa6qZpR5e27AyxaWUjqW6Vx959F51ewZ5jwu4x4fJb8eTZ8OTbyC0JYgrs9WVs5jV928mYZt3o2AkdO6BtK3TugtbNsOnlviy0ou9ppOLps2oYrGC0aaLaktO39Pijsbi1SYcmxxH5t5FIJEcHMZQGeUIIC1Csquqm4Q9p6MyaNUtdvXr1UX/epUuXcupe5aIkxwej5bVtCMW54r532dUR42cXTuLSOcUjHdIRIatmqemqobpNE8wb2jdQ3VZNJBXpPcdr9lLhrqDcXc4Y1xjK3eVUuCvIMX+ybPpoeW2PdTKRKJE33qDrlVdI7thBqqEBNR5Hsduxn3aa5mfOz8dQkI8+Nxe9x4MwDN8vBdmsypLFSyn2j6OzMUa4I0GkPUFn0x5/s9YU0F/ipHB8Di6fBb1JwWDS4/Saceda0RkOkAnPpDTx3PAhNH4EnbWa/zkZhVRUE9epmJahToYHuYjQRLLZDUWzoWCmVuIud4KWpT4OSisOJ/Jv9/hlJF5bIcQaVVVnHey8oVSxWAT8Dq2CRZkQYhrwPyPZKER6kCXHO3kuC899YyE3Pr6WHzz7Met3d/HjCyZg1B/b5dYUoVDmKqPMVdZbpzmrZmmKNrG1cyvbQ9t7OwMu3raYaCra+1iP2dPraS6wFxCwBgjYAgRtQXItuRhkDd2jgs5uw7XoAlyLLgC0CYPRFSvoevGfhP/1rwGe5j0Isxmd2415/Hh0Lhd6vw/LtGmYJ0/BEMj9RPEoisDsFkxYONA3vKeqRlt9hN1bO6mr7uD9JbWo2YFJIaEIXH4LDo8Jm9uEPceMK9eCO9eKO2DFZNUjPGWaP3niZw8cTCalied4x8ClYyeEGyDSDNUvwPuP9gtAp2WXzU5tIqHJoWWp86dDbpVW9s7m1yYVyoYrEslRY6iNQuYASwFUVf1ACFE2jDEdFOlBlpwIuKwGHvryHH7zykbufWs7mxrD/PHKGfjsx5cHUhEKefY88ux5nFx4cu+4qqo0xZrY3rmdbaFtmnDu3MZLO14ivJ9MndfsJWgLErBqornYWUyZs4wSVwl5tryjeUsnFEII7AsXYl+4EPWXvyAbDpPavZvU7gbSzc2k29vIRqOkdu8muX0Hic2byLS0ot7/AAA6txt9bi6KzdZTSaO0r/W304GwWFAsFq10nccz5KYpfVU1PBRVeZi7CFLdGRLRFOlkhmQiQ6glRkdDjI6GKOGObtobOoiFuun/w6qiF1gdRmxuE95CO/4iB/4iB94CG3rjXhVbdAaw+7VlMLIZTSyHG6HxYy0j3R3ut3TBrvdg3TP7Ptbk1Bqs2Px9zVZsfk2855Rp4tpo67F2eOAIeMQlkhOVoQjklKqqob3elA7uy5BIJJ8YnSK45fwqJuQ7ufnpj/jMnW9z71WzmFx4/Pt0hRAEbUGCtiALChYMOBZNRWmKNtEYbaQp1kRjrFHbjzVSG65lZcNKYum+OsEmnQmP4mHx0sWUOkspcWqVNipzKjHrj4xvVtJTXaOnE6B5/PhBz8smkyTWrSex7mO6t24j3aaJ6Ojbb+83A917fYsFQ0BrkKLPzUXv9aI4HVgaGulsa0ex23u6E/YsNm1fWCxai3KTDoOpT9QGSvet55zJZOlqidPZpDVEiXUliXclCXck2LammQ3LdmuxKIKcoBVfoR2n34LLb8Hls+D0W7A6jYMLeUWnVcpwFULhAX7ljTRD2zaItWoVOaJtPds9+521UL9WG+upWT4AnUlrtGL1aj5pvVmbYGiwaNv91wZLTyk8V18mu//a6JBiW3LCMRSBvF4IcTmgE0JUAt8GVgxvWBKJpD8XTitgjN/ODY+s4ZI/reDXF0/moumFIx3WiGEz2Ch3l1PuLt/vcVVVaUu0sSO0g51dO6kJ1bBmxxq2dGzh9drXezsH6oSOMldZb9fAKm8V4zzjcBplx7bhRDEasc6YjnXG9H2OqamU1vo71EW2K0Q2kdDK1dXVkarfTaq5iXRDI/G1a0m3t6PG4ziBhqeeOsATKj2i2YbO1tPy22xCGE0IoxFhMiFMRhSzBcViRlgsOM0W3FYLwmxGybOilJsRC81E02baQ4L29ixtzSl2b+lg86qmgT1NjApOnyaanX4LNpcJo1mH0aLHaNFjsugxmHU4cswYLYN8DNtzteVgZDPQWaNNKkxGNE90rA266rVMdawd0gltLJ2AVHzgOp04+HOAJrgNZtBbhrY2WLQSeiZHnzjXW/oE+R6BbrSDya6JeOnFlowihiKQvwX8EOgGHgdeBX42nEFJJJJ9mVTg4vkbF/LNv63lu09+yEd1If77/CoMOpnZ2RshBD6LD5/Fx+zgbACWRrXJIKlsivpwPVs7t/Z2D1zZsJIXtvdlLgvthb1dA6s8Wj1on+Xw2lFLDg1hMKD3etF7vUM6X02leGvJEuZPnUY22tPmOxIhG4lqbb8je+1He7oWxuJkOztRk0nU7iRqdzfZRAI1HkdNpQ76vDk9C0BWMdDtziPhyCduD5Cw+IhFPLTWuagVDjJi8I9aiymL0wEerw5/0EhukR1Xnh2dzYbSYy/BYDhwRtpTri2Hg6pqYnmPvSPRBd2hvrJ4iS7tWDoOqcS+61QcEiFINe11LD508b0HnVET4vo9a22ZGU/CVl/vfu85erP2mD2ZcZNDE9o6o2Z30Rn7be913b3HdMaB2zJjfsJzUIGsqmoMTSD/cPjDGRpykp7kRMVnN/HodXP55UvVPLh8Jxt2d3H3FcefL3k4MSgGSl2llLpKObPkzN7x1nir1nK7rbpXOP+r5l+9x10mFyWOEoqdxYxxj6HSXUlFTgX5tvwh+2IlRx5hMKDabBgLD6/V9/5Q0+lesZyNx8nGE6jxWE82O042HkPt3Y6jJuJ7bdeSjW/SxmJxUok0yRSkkiqpFKQwkNZbiJs9xCwBYrYgm5oKqN6kAt0Yu7fh6dhITscmXF07sCQ70JlNCKtFy3KbzVrme69FMRlRXC70OR50Hg96rwddjgddjluzvrhcvXaTvn9AoZWrM1q1hilH7B9R1SYopmKQ7tbWA4R1TBPRyYhWESQZhUw3pJN7rbvpbm7AYbRqY7F2yCS1a6a7e88hFde2jxSKYaBo7l2bezLl5n6Z8UHGesW8ad/x3nON+z+mM8qM+ggzqEAWQiwe7BjASFaxkJP0JCcyBp3CTxZNZEqhix888zGL7nybP105k6kj2HnveMBn8XFSwUmcVHBS71g4Ge5ttb0jtIParlpWNa7ixe0v9p5jM9iocFdQmVNJpbuyd+02y9fjWEXo9ejsdrDbh+X6aiazj+jORKO0N8Ro3t1NQ4OdRvscGoPzADAoGdyGKFYRxZKNYMmEcKWbsSXbULuTZKPRnkx4N5lQiEwoBIOVcDUYesWyzulEcTkHTIpUbPZeO4pis2mZbLsdpWetc7kQuiG0kxcCrB7gk9dxXzfUUmDppJa1ziT7lvSe7X6iO5PqE9e928m+9WDb6e6B9pRYa99Y/3UqPngHx0NhgMjuEeFGm9YFsn8NboNVKxcYnLSfTHlPFt1g1jzmMjM+ZA6UQZ4P7EKzVbwLyK8yEsko4qLphVTmOvjao2v4/L3v8PMLJ/GF2UUjHdZxhcPoYHZwdq9NYw/hZJhtndvY3LGZLR1b2NK5hSU7l/B08unec/wWP5U5lVS4KyhxapnnYkcxQVsQRcgPqRMZodOhs9vAPrARig2tExeAmlVp2x2luaaL5powrbvCtLQniIWSPRcBU44ef7EDd8DaU6rOjNmsQ68HQzaBVY1Cl9ZaPNMVItvVRaYz1OPxDpHtCpFpbSO5bTuZri6y4fDgwro3eKGJZYcDnd2O4nRqbc9dThSnS8tWO5woDjs6hwPF7tAmTjocPZMoHQj9MLWd1xtHTym8TLovu73H671HwO8tqjPJfuf0P7bX41Pxvmx7pKlnP6bV4453DCEo0Seo9/jAdcZ+kzWte2XC+0/s7PGQH87+MZoJP9D/0iBwFnAZcDnwT+BxVVXXH43AJBLJwZlU4OKFG0/iW4+/z83PfMR7O9v54flV5NhGyYfEcYrD6Nine6CqqjTHmtnaubVXNG/p2MITG58gmU32nmdUjBQ6Cil2FPeK5iJnESXOEoLWIDplCNk5yXGPUAS+Qju+QjsTFvaNZ1JZQi1xmnZ20bgjRGttmM3vNZGM76eSBWBzGXH6vTh9hTgDFtxTLHgL7OQErCh7zV9QVRU1FiMTiZKNhMlGo71LJhIhG46Q6WjXPNx7/N1dYVLNTXRv2UKms5NsNLrfOAbcm8WiiWuHQxPStn7bdkefuHZomW7D9m10Fxaic7lQ7HaE2Tz6bU06vbYY9+0GOSx01moTNQexqZBOaCI6GdXsLclYnzjfkxGPt/fYYPqL+p5zPgn9J2n2F9COPMj/+pG5/2FgUIGsqmoGeAV4RQhhQhPKS4UQP1VV9a6jFaBEIjkwOTYjf712Dv/3r83c8+Y2/l3dxH+dO54vzipCUUb5h8hxhBCCgE1rXrKwoE/R7GmEUhuupTZcy66uXdR01VAbruWdhnfo7ueb1Ct6Cu2FvcK5/zpoC2JQZDOUEx2dQcGTr7XQrlqg1fdWVZXuaJpwR4JUd4Z0d4Z4JEVXa7xnSVC/qYNN7zb2VttQ9AKX34rDY8LuMePIMfduW+w2TF4XxiI9hr1rPR8ENZnsEdPhHiEdJhMOk91rOxPpGeuZVJlqaOgdUxMDJ/d5gO2/u61vQAhtAmOPqFYsVq0KidGkVR2xWAaU/NP7fOg83r5Mtq3HPmK3I4zHSTLBXawtw0E2s28mO92t+cn3WEoOZ/9ofXk4TA74O0ePMP40mjguBe4Anhv+sCQSyaGgUwTfO2cci6bm8+Pn13HLsx/z7No6br90Ovluy0iHd0LTvxHK3Ly5A45l1SwtsRZNPHf1COjwrl6vczwd7z1XIMgx5+C1ePFb/L1VOvwWP7nW3N7Fb/HLroInGEIIzHYDZvuBX/d0KkNnU5y2+gitdRG6WuKE2xO01IaJh/dfucNk1ePwmnH5LBSMy6F0ig+HZ/Da4cJoRO/xgOfwvcdqKqWJ7K4uMl1dvL9sGZNKSzV/dSSiTZ6MxvpEdiKuebFjMbIdHT1ZcC3LrXYfeOKesFh6a3crLic6h3OgN9vpRHE6enzaPcecTk1kWyxD82Mf6yi6vomcR5qlS4/8NY8QB5qk9zAwCXgJ+KmqquuOWlQSieSwGBd08OT183hmbT0/eX4d59+xjN9dMpUzJxzB2emSI4YilN6s894+Z1VVaY239ornxmgjLfEWWuOttMZb2RHaQUu8hfR+mkR4zJ4BwnlPd8GALUDQqjVfsRqG4cNOMqrRG3S9to1xA7+rkU5miHR0E25PkIim6I6l6Y6liHR009WaoGVXmG3vt/DWE5vxFtpxeMyYrHrMNgOefBv+YgeePBs6/Sf31wuDAX1ODuRohfSSbW04hzJJbz+oySSp5hbN/hHu0jLYsZhW9i+s1dvW/NfadqqhgcSmjWRDXUOzixiNWvv0vDwMeXkYgkF0Pi96nx/TmHJt8qPbjeJwIOQEuWOKA2WQrwSiwHeAb/fz+whAVVVVVtKXSEYhQggumVnIzJIcbnxsLdc9vJqr55fwn2ePw2WRmcVjBSEEfqsfv9XPzMDM/Z6jqiqd3Z00x5ppjjXTEm+hKdZES6yld2x923raE+37PNZhcOCz+vCavb3ZaK9F28619GSkbbk4DI7R7/eUfGL0Rh3ugBV3YPAvTh2NUXZ82Mqu6nbC7Qna6tLEI0nSySyg+abtbpOWcfZbyKtwUzDOjdM7cr9iCaNRKwF4GGUA1XRaE9Q9mew9zWsyXV1ko7GeEoAxMp2dpBsa6d6yhchbb6HG4/teTFF6xbLO7da2c3LQeXLQuXr2XVqWWrH3t4HYEFar/BscAQ7kQR61X3VkHWSJ5OCU+Ww8+40F/PrljTy0YicvftTAf549lktnF6OT3uTjAiE020WOOYdxnnGDnpfKpLSW3Htac/es2+JttMZbqW6vpjXeSjS1b8bMrDNr1g2rvzcb7bf4ybXl9gppv9WPSSdrcR/v5ARt5ARtzDinpHdMzaqEWuK07ArTVhch3J4g3J5gx4etVK9oAMDpM1MyyUfpZC/5Y93oDceGLUHo9QMy2UNBVVXURIJUXR3J+nqtgsiAJUSms5NUUxOJjRvJtLejJg8yCU4IFIcDQyDQUw3E1iegbfa+bZcTvd+PPhhEn5uLzu2WwvoTMEy1VoYXWQdZIhkaJr2OnyyayCUzC/npCxv44XPreGp1HX++eia5jsF9hJLjC4POQKGjkELHgduTx9NxWmOtNMebaYlp2ejmWN/2utZ1vB57fcDEwj24Te5esdwroq25Wka6xxvttXjRK8fkx45kEIQiejPPlbP6rFxqVqW9IUr95k52bWijevluPl5ah8mqZ9zcIBNPLsCTP7onaR0OQgiExYKpshJTZeWQHpNNJDR/dWeITKhTs39Eo1pnyD1VRDpDpJqbyEaiZNo7SO2q6+kMGSUbi+0/FpMJfTCA3ufXqobY7QPK72lVQ+z7luLrOXfYyvEdI5zYdy+RnCBMzHfx5PXzWPzhbn7wzMdcdPcKHvzybMYGHCMdmmQUYdFbKHIWUeQcvJ62qqp0Jbt6LRwDlri23tS+ifZEO1k1O+CxAoHH7OkV0n6Lv3e9R0T7rX48Zo8U0sc4QhF4C+x4C+xMOa2QdDJD3aYONr/XxLpl9Xz0Rh3+Ygd5Y1wEx7jwFdpxeM3HTHb5SKKYzShmM4bA4c0VUbNZsrGYJrBbW0g1NpFuauxZN5FubSXd0kJmx47eqiEMoZ26sFq1RjEuZ68NRO/1YCgoRO/zovN60efmYggE0Hk8x53HWr4DSSQnCEIILpxWwBi/nWsfWsXFf1zBHZdP57RxuSMdmuQYQgiBy+TCZXJRmTN4hiydTdOeaKcl1kJLvKXXI71nvyXWwvpWzR+tMrA5hSIUPGYPAWuAEmcJpc5Sip3F5NvzybPl4bP4pIA+xtAbdZRO9lE62Uc8XEn1Ow3Urmtjw9u7+eiNut7zrC4jTq8Zh9eCw2smt9hBuvsgzUtOcISioOvJ/FJYwMEc36qqonZ3k41ENI/1XmX5tHGtCkgmEibbFSYTCpHavZv4hx+SaWvb96I6nZZ93tN5sacbo85uH9CdsTeT3TM2FKE+Ush3GInkBGNSgYt/fHMh1z60ii8/uIpZJTl89ZRyzqoKyLrJkiOGXtH3VtE4EOlsmrZ42wDxvEdMN0Qa+KD5A17e8fIAEa0TOnKtueTZ8si355PuTNO5tZNCu2YjybXmym6FoxiLw8iMs0uYcXYJmUyWtroIHQ1RutoShNsSdLUlaNoRYtuaZrJZ7XXvWLuKwvEeCqtyyBvjOiEzzUcKIYRWL9psRu/zHfLjs93dZFpbSbe2kmpuJt3UTLqlpad5TIRMNNJnBand1bu/v8mL4rbfHYlbGhakQJZITkDy3Rae+8ZCnlxVy/1v7+CGq4/6NAAAIABJREFUR9YwLuDg9sumMT4oC9RIjh56Rd9b6m4wEukE9ZF6GqIN2hJpoDHaSEO0gTVNa2iMNvLK8ld6zzcoBgrsBRQ4CjTR3COcC+wFFDuLsRmOP+/rsYpOp5Bb4iS3ZN/3nUwmS/OOLpa98j66hMIH/6pl7as16PQK7oAFl9+K02fGZDNgNOswWQ3kV7oPWKdZ8slRTCaUggIMBQfPVvdHTac1z3QkonVrjEZo6uwctjg/KVIgSyQnKBajjmsWlnHlvBJeWtfIz17cwGfuWs7/b+/O4+uq6/yPvz53zc1dkmZt2qbpXihQChQosqUIgiyCDALquI6izqCOy6jo+INZXccRBR0REBkVVERkG0WUspStrG0pS9e06ZJu2ffkfn9/nJPbmzSlLW16c9P38/E4j/M93/M953xvvz3JJ9/7Pef79Qvn8LcnT9bTzzJqFIQKmF48nenF04fd//AjDzPrhFnUt9VT31pPfVs9G1s3Ut9Wz7Jty2jpaRlUvrSglJpUDZNTk6lJ1WSWycnJFIQUXI0WwWCAqhnFVBxt1NaeQE9XH5tWNrHxjSaaGjpo3NJO3fId9PcNHuteMiFO9ZwSkiUFFKYixIsiJEtjxIuj+pYshywUyrzOLvPC0XycKEREDg+hYIB3HTuBt00v5Yu/fZmv37OcxSu3862/mUtRod6bLKNfyELetNyp4afabelpYWPrRm+WQn/ilbqWOp7Y+AT3rLpnUNmqeBWTU5OZkpqSCZynpKYwITFB455zLFIQyoxjHuCco78vTW9XPx0tPaxfsZO65dtZ9kg96f4hY9uDRrKkgFRZAcmyWCaALkxGiKUimXQwrOE5ogBZRHxliSi3fuhEbnliLd/642uc/4PHuf7Kecyf8tanjBUZDVKRFKnSFEeWHrnbvvbedupa6ljfsp51Leuoa6mjrqWOB9c+SGtPa6ZcyEJMSk7KPDA4EDjXpGqoKKzQNy45YmaEwkFC4SCxZITSiQmOO2cyLu3oau+lo6WH9mZvNsDWHZ20bO+iZXsn217YRlf78A+IJUsLmDynhMlHlVJRkySWihAMKmg+3IyqANnM4sCjwHXOuftzXR+Rw00gYHz8jGmcNLWET9/xIlfc9DSfP2cWnzxzuiYXkTEpHo4zp3QOc0rnDMofmKWwrqVuUOC8rmUdT21+atC7oGOh2KBhGlOKpmQC6KJo0aH+SIL3mrlYMpIJmofT1+P1One09tDZ0kNHSw+drT1srWvljWcbeOXxTZmyBYkw8aIIhUVRClMRxo0vpKImRfnkJAVxfdM2Fo1ogGxmtwIXAludc0dn5Z8HXA8EgZudc9/0d30Z+M1I1klE9u7Y6mIe+MxpfPX3y/nOn15n8artfP+KeVSkND5TDg/ZsxTOq5g3aF/apWlob6CutY665l0B9Ks7XuXhuofpd/2ZssXR4kFDNQbS1clqCsN7ntZZRl4oEiRVFiNVtvujZv19aRrWNtO4pYP2Zi947mjupr25h8bN7bz+9JZM2cS4KMWVhYyrilM1vYjqI0ooSChozncj3YN8G3ADcPtAhpkFgRuBc4B6YImZ3QtMBFYA+g0sMgokC8L84Mp5nD6jjP9373Leef3jfPc9x7LwCL03WQ5vAQtQlaiiKlHFgqoFg/b19vdS31af6XEeWJ7e/DT3rr53UNmKWAWTkpOYnJpMdbKa6mR1pgdab9rIrWAowISZ45gwc/hpprvae9m2vpWtdS00bumgcUsHrz25mWWP1INBxeQk5TUpyibGKZ2UpGJKUsM08syIBsjOucfMbMqQ7JOAVc65NQBmdidwMZAA4sAcoNPMHnRuyDRMInJImRmXn1jN8TXFXP2rF/nIbUu44Jgqrjn/CCaNU++XyFDhYJipRVOZWjR1t30dvR2sb/XHOjfXsb51PfWt9SzeuJhtndsGla2KVzGteBqTEpMYHx9PVbwqs5QXluuBwRwriIepPrKE6iN3PaOR7k+zta6VDa/upP61RlY918Arj/UBEC0MMfmoUmqOKiFZFiNeFCFeHNX7nEcxc25kZ6jxA+T7B4ZYmNllwHnOuY/52x8ATnbOXe1vfxjYvqcxyGZ2FXAVQGVl5Ql33nnniNZ/OG1tbSQSw49pkvymtt2znn7H/63t5YE1vTjg/Klhzp8WJhrMj7HJatuxayy0bXe6mx19O9jWt40tvVvY0ruFht4GdvbtpD3dPqisYaSCKYqCRZmlOFQ8eDtYTCwQGxMPD+Zr+zrn6OuAzp3QusnRugmyhq5jAYhXQqraSE6AUEH+t9X+ykXbLly48Hnn3Py9lRt1f4I6527by/6bzGwzcFEymTyhtrb2kNQr26JFi8jFdWXkqW3f3DuALzZ18o0HX+UPSzezZHuQr15wJBccUzXqfxGrbceusd62Hb0dmYlRBpZtHd6Mg1s7t7K+Yz3Nbc27HVcQLKC8sJzKwkqq4lVeT3SialBvdD6Mgx4r7ZtOOxo3t9Pe1E1HSw/bN7ax5sVtbHq2C4BUWZTy6iSVU4uYMreU4srCUf9z9UCN5rbNRYC8EajO2p7k5+0z59x9wH3z58//+MGsmIjs3cTiGDe873g+sGAH1923gqt/9SK3T6njUwunc+bMcr2IX+QgKwwXMq14GtOKp+2xTFdflxc0d25lW8c2GjoaMkH0lo4tPNfwHFs7tg56gBC8V+ANBMvj4+OZkJiwK5iOV1EWKyMY0DCAgyEQMEonJga9VePUv5nBtvXesIztG9rYur6V1S9u48m7V5Eqj1E1vYiCRJiCwjCFRRFSpQWkymLEx0U1pnmE5SJAXgLMNLOpeIHxlcD7clAPETkAJ08r5f5Pn8adS9Zz/cMr+cjPljCtPM5HT53K5fOriYT0w1vkUCkIFVCdqqY6Vb3HMn3pPrZ3bs9M1z3QG72lfQub2jfx/NbnB737Gbz3P1fGKweNgx6ajofjY76nc6SY2W5Tbbfu7KJu2XbWLt3Bxtcb6eroo6978B82FjASxVGSpd7EJ6mymJcu9daaNfDAjfRr3u4AaoEyM6sHrnXO3WJmVwN/wnvN263OuVf287wXARfNmDHjYFdZRPZDMGC8/+Qa3nNCNQ8u28yti9fyz/cs55Yn1vK184/k7UdqAgWR0SIUCDE+Pp7x8fEcV3HcsGXaetp2G84xEFC/0PACDR0Nu/VCx0IxSgtKKYuVUV5YTmlBKaWxUsZFx1FcUDx4HS0mHNQr0N5MsqSAo8+cxNFnTsrk9femvQlPdngTnbTu6KJlRyet27vYsGIn7c09g84RCBpF5TGKKwspriikeHyh9yq6ykJiycih/kh5aaTfYvHePeQ/CDx4AOfVEAuRUSQSCnDJcRO5eN4EFr2+jX9/YAUfu/05Tp9ZxvVXHkdJXD+QRfJBIpJgRmQGM8YN3wHVn+5nW+e2TBC9pX0L2zu3s71zOzs6d7CmaQ3PdD5DS0/Lnq8RTlAcLWZcwbjh10MC67ReaEUwHNj1zubZu796rq+3n7ad3bRs78wE0c1bO2ls6KDulR2k+3a9kCExLkrllBTlNUlKJyQYVxUnVVqAqcd5kFH3kN6+UA+yyOhkZiw8ooLTZpbxv0/V8c0/vsbf/XwJv/rYAmIRjWMUyXfBQDDTCz2PeXss19vfS1N3E43djTR1DVl3N9HY5a23d25nVdMqmrqb6OzrHPZchpG6M5XpgR4IngcC6uzgeiCdjCQJ2OEzzCsUDnq9xZW7P3SZTjtad3TR1NBB45Z2tta10rCuhdUvbss6PkDx+EJKquKUTIhnxkonxkUP228B8zJAVg+yyOgWDgb46GlTmVBcwKd++QJX/+oFfvKBEwjpoRKRw0I4GKa8sJzywvJ9Pqazr5Pm7mYauxoHBdQvvf4SReOLaOpuoqmriU1tm1ixfQWN3Y30pnuHPVfAAhRFijLBdFG0iHEF3joVSZEMJ0lGdi2pSCqTjgbHVlAYCHjDLYrKY9QcXZrJ7+7opXFLBzs3t7NzczuNm9vZtKqJN55tyJQJRYPEEmFiiTAFiTCxRMR7aNDPiyUipMpjlEyIj7kxz3kZIItIfjjv6Cr+9eKj+fo9y/nne5bzb5ccTVhBsogMIxaKEQvFGB8fPyh/YsNEahfU7lbeOUd7bztN3U1eYN3dSGNXYybd1NXkBdXdTdS31bN8+3Kaupv2GFQPCAfCuwXNyUiSokhRZvrxkoISSgpKMuniaHHeTd4SLQwzfloR46cVDcrv7uxjx8Y2dtS30bytk662Xjrbeuhq8wLqzrbe3R4aDEeDVNQkKamKkygpIFlSQKKkgMS4KPGiCIE8/LmfX63p0xALkfzxgQU1NDR3ccMjq/jzigYunFvFJcdNZF518ZjqpRGRQ8vMSEQSJCIJJiUn7f0AX3d/N609rbT0tNDa0zpoGS6vtbeVTW2baO5upqm7CcfwE6ylIqlML/XAcI/s9G55BcVEg9GD9c9x0ERjISbMKGbCjOI9lunr6aervZfO1l52bm6nYW0LDWubeWNJA90dfYPKWsAoqSqkvCZFZU2SyUeVemOpR7m8DJA1xEIkv3zhHbOYV13M71/cyB1LNvDzp+pYMK2EL513BMdP3v2BExGRkRINRonGopTFyvb72P50f2YMdWN3Izu7dnrpLi890GO9rWMbKxtXvunYavAmc0lFU6Qiqczwj+x1UaSIVDSVWaciqUzPdi7fTx2KBElEgiTGFVA+Ocnsk3f1+vd09tHa2EXbzm5ad3bRuqOL7fWtrHt5O689uZmFHziCOQqQRUS8np6z51Ry9pxKWrp6ufv5em54ZDWX/uhJzj6ykq+efwTTyvNvKlkRObwEA0FKY95r7PZVd393ZrjHQC/0QLqlp4Xm7uZMur6tnhU7VtDS0/KmgTV4bwNJRVKDAudBaX97YLhIIuKVT4QTIzrOOhILURpLUDph8M9057yHBSOx/Ag986OWQ2iIhUj+ShWE+fCpU3nP/Gp+tngtP3l0Ded+/zE+dvo0rl44g3g0L38siYgMKxqMUhmvpDJeuV/H9fT30NLTQkt3C809zbR0t3jbft7Q9LqWdZl0V3/Xm557YJx1MpIkEU4MGmudDCdJRBKZffFwnMJwIfFwnHgontkuDBcSDuz7O63NLC+GVgzIy99EGmIhkv/i0RBXnzWTK06czLf++Bo/XrSa37+wkX+/5GjOnrN/v0hERMaaSDBCWazsLQ0FyQ6uBwLptp62zJjqgfHVbT1ttPR6+7Z2bPXyetv22ns9IBwIe8FyyF/8dCzsTR5TGa+ksrCSVCSV2VeTqmFcwegfWpeXAbKIjB3lySjffc+xvPekar72++V87PbnuPT4iVx74VEUFWrGLRGR/XUgwTVAb7qXtp422nraaO9rp73XWzp6O+jo66C9t53Ovs5BeZ19nZl0Y1sjy7YtY0fXjt3Ofe0p13LZrMsO9COOOAXIIjIqnFBTwr1Xn8YNf13JjYtWs3jVdr5z2bGcMWvf36MqIiIHLhwIZ15pdyB6+3vZ2rnVC7R72+no62B60fSDVMuRlZcBssYgi4xNkVCAz79jNufMGc/nf/MSH7z1WT522lT+6bzZREOaiU9EJJ+Eg2EmJibmuhpvSf69uRlvDLJz7qqioqK9FxaRvHPMpCLu+/RpfGBBDTc/sZZ33/gkKza15LpaIiJymMjLAFlExr6CcJB/u+RofvrB+Wxp6eKCHz7ONXcvZXtbd66rJiIiY5wCZBEZ1c6ZU8kjX6jlo6dO5bfP1VP7nUX8/Ml1pNPDz2YlIiJyoBQgi8ioV1QY5usXzuFPnzuD4yYXc+29r/Denz5N3Y72XFdNRETGIAXIIpI3ppcnuP2jJ/Hty+ayYlML533/cW5bvFa9ySIiclDlZYBsZheZ2U3Nzc25roqIHGJmxuXzq3no82dw0tQSrrtvBVeqN1lERA6ivAyQ9RYLEakqinHbR07k25fN5VW/N/nmx9fQ15/OddVERCTP5WWALCICg3uTT5leyr8/8CoX37iYpfVNua6aiIjkMQXIIpL3qopi3PKh+fzo/cezrbWbi29czDV3L2Vzc2euqyYiInkoL2fSExEZysw4/5gqTptZxvceeoNfPlPH717YyIdOqeHvazXrpoiI7Dv1IIvImJIqCHPdu47ir1+o5aK5E7jlibVc+MMnWN/Sn+uqiYhInlCALCJjUnVJIf91+bHc8w+n0p92/MczXTz0ypZcV0tERPJAXgbIes2biOyruZOKuffqU5mQCPCJXzzPv963gvrGjlxXS0RERrG8DJD1mjcR2R8VqQKuOamAy0+o5rYn13LGtx/hE//7HC9t0NsuRERkd3kZIIuI7K9I0PjWZXN5/Mtn8Ykzp/PM2p1c+qPFfOP/XqWrV+OTRURkFwXIInJYmVgc48vnHcHjX1rIFSdW85NH13DRD5/gxfWNua6aiIiMEgqQReSwlCwI841L53LbR06ktauPd//oST5++3O8sknPNoiIHO4UIIvIYa12dgUPf+FMvnDOLJ5es4MLfvAEn7njRba1due6aiIikiMKkEXksJeIhvj022fyxJfP4uqFM/jj8i2c89+PcvcL9Tjncl09ERE5xBQgi4j4imJhvnjubB787GlML0/w+d+8zBU3Pc39SzfR3acH+UREDheaalpEZIgZFUl++4lT+MUzdfzk0TVc/asXKYlHeM/8SXzkbVMZX1SQ6yqKiMgIUoAsIjKMQMD44ClTeP/JNTy+cht3PLuenz62hlufWMsl8ybyiTOnMaMimetqiojICBg1AbKZHQl8FigD/uKc+3GOqyQiQjBg1M6uoHZ2BRt2dnDz42v49XMbuOuFei4+dgL/ePYsppTFc11NERE5iEZ0DLKZ3WpmW81s+ZD888zsdTNbZWZfAXDOveqc+yRwOXDqSNZLROStqC4p5F8uPponv/J2rjpjGn98ZQtv/96jfOV3S9nU1Jnr6omIyEEy0g/p3Qacl51hZkHgRuCdwBzgvWY2x9/3LuAB4MERrpeIyFtWEo9wzTuP5LEvLeQDC2q4+4WN1H5nEdfd+4peDyciMgbYSL/CyMymAPc75472t08BrnPOnetvXwPgnPtG1jEPOOcu2MP5rgKuAqisrDzhzjvvHNH6D6etrY1EInHIrysjT207do1k227vTHPv6l6e2NhHAJhbHuSUCSGOLQ8SCdqIXFN20X07tql9x65ctO3ChQufd87N31u5XIxBnghsyNquB042s1rgUiDKm/QgO+duAm4CmD9/vqutrR2xiu7JokWLyMV1ZeSpbceukW7by4C129u5/al13PfyZl54qZtENMSZs8t5x5xKamdXUBQLj9j1D2e6b8c2te/YNZrbdtQ8pOecWwQs2peyZnYRcNGMGTNGskoiIvtlalmcay86iq+dfyRPrt7BA0s385fXGnhg6WZCAePkaSWcc2Ql5xw1nonFsVxXV0RE9iAXAfJGoDpre5Kft8+cc/cB982fP//jB7NiIiIHQygY4IxZ5Zwxq5z+tOOlDY08tKKBP69o4Lr7VnDdfSuYU5XinDmVnDOnkqMmpDDTUAwRkdEiFwHyEmCmmU3FC4yvBN63PydQD7KI5ItgwDihpoQTakq45p1HsnpbG39e0cDDKxr4wV9Xcv1fVjKxOMbZR1bwjqPGs2BaKcGAgmURkVwa0QDZzO4AaoEyM6sHrnXO3WJmVwN/AoLArc65V/bnvOpBFpF8Nb08wfQzE3zyzOlsb+vmr69u5aEVDdy5ZAM/f6qOicUxrjixmsvnV2vGPhGRHBnRANk599495D+IXuUmIoe5skSUy0+s5vITq+ns6ecvrzVw57Mb+N6f3+D7D7/BqTPKuHjeRM49qpJkgR7wExE5VEbNQ3r7Q0MsRGSsiUWCXDh3AhfOnUDdjnZ++1w9f3h5I1/87cv88z0B3ndSDZ88cxoVKfUqi4iMtJGeKGREOOfuc85dVVRUlOuqiIgcdDWlcb547mwe+6eF/O5Tp3DBMRP4+VPrOP3bj/Av973C83U76e1P57qaIiJjlnqQRURGKbNdD/h95u0zuOGvq7j9qTp+tngdhZEg86eUcM6RFZx79HgqkupZFhE5WNSDLCKSB2pK43znPcey5Gtn8+P3H897TphEfWMHX//DKyz4z79w5U1P8eCyzaTTIzs7qojI4SAve5BFRA5XJfEI7zyminceU4Vzjjca2nhg2Wb+8NJG/v6XLzCrMsGnz5rJuUeNJxLKyz4QEZGcU4AsIpKnzIzZ45PMHp/ks2+fyf1LN/HDv67i03e8SEE4wHHV4zh5WgnnH1PFrMpkrqsrIpI38jJA1hhkEZHBggHj4nkTuXDuBB59YyuPr9zOs2t3cv1fVvL9h1dy0pQS3r9gMucdPZ5oKJjr6oqIjGp5GSBrohARkeEFA8ZZR1Ry1hGVAOxo6+au5+v51bPr+eydL1FVVMAnzpjGlSdNpiCsQFlEZDh5GSCLiMi+KU1E+cSZ0/n46dN4bOU2fvTIaq67bwU3PLKai46t4qQpJZw4tYSyRDTXVRURGTUUIIuIHAYCAaN2dgW1syt4Zs0OfvLYGu54dj0/W7wOgGllcU6aWsKJU0o4fWaZJiQRkcNaXgbIGoMsIvLWnTytlJOnldLTl2b5pmaeXbuTJWt38uCyzdy5ZANmcGJNCecfM57TZ5UzpTROMGC5rraIyCGTlwGyxiCLiBy4SCjA8ZPHcfzkcXzyzOmk047XtrTy5xUNPLBsE9fdtwKAaCjA7PFJ5k4q4tTpZZwyvZTiwkiOay8iMnLyMkAWEZGDLxAw5kxIMWdCis+ePZNVW9t4cX0jr29p5dUtLfz+hY384un1mMHMigQzK5PMqkgyqzLBrPFJakoKCQX17mURyX8KkEVEZFgzKhLMqEhktnv707y8oYnFq3awbGMTy+qbeXDZZpw/eV8kGGDSuBipWJiiWJjiQm9dFAtTGo8wubSQySVxJhQXEAsHMdOwDREZnRQgi4jIPgkHA8yfUsL8KSWZvI6ePlZvbef1hlZWNrSysamT5s5eGjt6WLejnebOXpo7ezNB9IBIKECxH0QXxyIUFYZ3bRdGMgF2cSySCbSLC8MkoiEF1iIy4vIyQNZDeiIio0NhJMQxk4o4ZlLRHsuk047t7d1s2NnJhp0dbGr2gujmjl6aOnpp6uxhw84Olnd62529/Xs8VzBgFMfCFPlBc7IgTFdLF3/auYxUQYhkQYhULEyyIEQy6q8LwqRi3joRDemBQxHZq7wMkPWQnohI/ggEjIpkARXJAk6oGbfX8l29/bR09tLkB8xNHT00DQTUnT00dXi90k0dvTR39NDQkmbNigZau3rp7kvv9fyJqBdIJ6Ih4tGBdTCTLoyESPjbu/Zn5UVCxCJBoqEA0VCQcNDUqy0yxuRlgCwiImNXQThIQTi4z+9iXrRoEbW1tQB09/XT2tXnL72ZdUtnHy2ZbS/d3t1HW3cf7d19bGvt9tI93nZvv3vzi2Yx88ZfR0MBouGBwDlAJLQrPZAfGdjO2hcKGuFgwF92pSPBXfsiIW97IB0OWibP285a+2n1lIu8dQqQRURkzIiGgkQTwQOeGbC7r5/27v5BQbS39vK6+vrp7k3T3ddPT1+a7szSvyvdu2u7pbN31/5eb3+Pv68v7ehP73tAvq8CRiZojg4JosPBAKGAEQgYoYARNCMY8JaBvID5+4YuZgSDg48ZtG9I3sC5hisbCu66Tua6A/v89MrGflLrG3fVaci1h8sbrk7q5Zf9oQBZRERkCK+HN0hJ/NC87zmddvSm0/T2O3r70oPT/Wl6+tP09Hl5Pbvl7Vp3DymzKy895DhH2jn60o502tGXTtOfdnT39dPvoD+dpj9NZl/a4a3T+GUHynjBfb9zmX0jEOvDM08e8CkCBmbmrTHMvN7/gBmGt2ZgOys/c8yQPBu27HDX8LYHylrmem9SH38Ng7e9LwV21cc7E5ljDe8zZOo4sC9rm+zrseu4gXN49rDfPw/D5u9+DQZd38u77IRJTCmLH3B7jjQFyCIiIjkWCBjRQJBoCDiwzu+cc25X0JwJoLOX4fKdo69/aNDurV946WWOOuaYQXl9ab9s/+DzpYecZ+h1HA7nIO3IpJ1z3raDtP+6lbRzmW0v4Pf+AHDsKuucww0p6/zPn9keuNYwZYdexzlIp6GfdOYYr56Dz5k5n/+HSOZzsOs6DNl2gz7vrnYaus8/1E/vft7sz/2m18j8Z9i9fidPK1GALCIiIocX84c8HKwAo6c+SO3sioN0NpF9oymPRERERESy5GWAbGYXmdlNzc3Nua6KiIiIiIwxeRkgO+fuc85dVVS05xfTi4iIiIi8FXkZIIuIiIiIjBQFyCIiIiIiWRQgi4iIiIhkUYAsIiIiIpJFAbKIiIiISBYFyCIiIiIiWRQgi4iIiIhkUYAsIiIiIpJFAbKIiIiISBYFyCIiIiIiWcw5l+s6vGVmtg2oy8Gly4DtObiujDy17dilth271LZjm9p37MpF29Y458r3ViivA+RcMbPnnHPzc10POfjUtmOX2nbsUtuObWrfsWs0t62GWIiIiIiIZFGALCIiIiKSRQHyW3NTrisgI0ZtO3apbccute3YpvYdu0Zt22oMsoiIiIhIFvUgi4iIiIhkUYC8H8zsPDN73cxWmdlXcl0f2TszqzazR8xshZm9Ymaf9fNLzOzPZrbSX4/z883MfuC38VIzOz7rXB/yy680sw/l6jPJYGYWNLMXzex+f3uqmT3jt+GvzSzi50f97VX+/ilZ57jGz3/dzM7NzSeRocys2MzuMrPXzOxVMztF9+7YYGaf838mLzezO8ysQPdufjKzW81sq5ktz8o7aPepmZ1gZsv8Y35gZnZIPphzTss+LEAQWA1MAyLAy8CcXNdLy17brQo43k8ngTeAOcC3ga/4+V8BvuWnzwf+DzBgAfB6ZwVTAAAIoElEQVSMn18CrPHX4/z0uFx/Pi0O4PPAr4D7/e3fAFf66f8BPuWn/x74Hz99JfBrPz3Hv5+jwFT/Pg/m+nNpcQA/Bz7mpyNAse7d/F+AicBaIOZv/wb4sO7d/FyAM4DjgeVZeQftPgWe9cuaf+w7D8XnUg/yvjsJWOWcW+Oc6wHuBC7OcZ1kL5xzm51zL/jpVuBVvB/OF+P98sVfX+KnLwZud56ngWIzqwLOBf7snNvpnGsE/gycdwg/igzDzCYBFwA3+9sGnAXc5RcZ2rYDbX4X8Ha//MXAnc65bufcWmAV3v0uOWRmRXi/eG8BcM71OOea0L07VoSAmJmFgEJgM7p385Jz7jFg55Dsg3Kf+vtSzrmnnRct3551rhGlAHnfTQQ2ZG3X+3mSJ/yv5Y4DngEqnXOb/V1bgEo/vad2VvuPTt8HvgSk/e1SoMk51+dvZ7dTpg39/c1+ebXt6DQV2Ab8zB9Cc7OZxdG9m/eccxuB7wLr8QLjZuB5dO+OJQfrPp3op4fmjzgFyHJYMLME8DvgH51zLdn7/L9K9TqXPGNmFwJbnXPP57ouMiJCeF/b/tg5dxzQjvdVbYbu3fzkj0e9GO+PoAlAHPXqj1n5ep8qQN53G4HqrO1Jfp6McmYWxguOf+mcu9vPbvC/usFfb/Xz99TOav/R51TgXWa2Dm/I01nA9Xhf2YX8MtntlGlDf38RsAO17WhVD9Q7557xt+/CC5h17+a/s4G1zrltzrle4G68+1n37thxsO7TjX56aP6IU4C875YAM/2nbCN4Dwrcm+M6yV7449RuAV51zn0va9e9wMBTsh8C/pCV/0H/SdsFQLP/NdGfgHeY2Ti/9+Mdfp7kiHPuGufcJOfcFLz78a/OufcDjwCX+cWGtu1Am1/ml3d+/pX+k/JTgZl4D4VIDjnntgAbzGy2n/V2YAW6d8eC9cACMyv0f0YPtK3u3bHjoNyn/r4WM1vg/1/5YNa5RtaheBJwrCx4T1++gfek7NdyXR8t+9Rmp+F9tbMUeMlfzscbv/YXYCXwMFDilzfgRr+NlwHzs871UbyHQFYBH8n1Z9MyqJ1r2fUWi2l4vyRXAb8Fon5+gb+9yt8/Lev4r/lt/jqH6AlpLfvUrvOA5/z79x68p9t1746BBfgX4DVgOfC/eG+i0L2bhwtwB95Y8l68b37+7mDep8B8///JauAG/EnuRnrRTHoiIiIiIlk0xEJEREREJIsCZBERERGRLAqQRURERESyKEAWEREREcmiAFlEREREJIsCZBEZs8zMmdl/ZW1/0cyuG+FrTjGzTjN7KWuJmNm7zOwrfplLzGzOSNZjD3W7zsw2Dqlb8Vs4T62Z3X8A9fjqkO0n3+q5RERGggJkERnLuoFLzazsEF93tXNuXtbS45y71zn3TX//JcAhD5B9/z2kbk05qMOgANk597Yc1EFEZI8UIIvIWNYH3AR8bugOM7vNzC7L2m7z17Vm9qiZ/cHM1pjZN83s/Wb2rJktM7Ppb6UiZvZhM7vBzN4GvAv4jt+DO93MFpnZt/xrvGFmp/vHBM3sO2a2xMyWmtkn/PwqM3vMP365mZ3ul73N315mZrt95jep29NmdlTW9iIzm29mJ5nZU2b2opk9mTWrXfax15nZF7O2l5vZFD99j5k9b2avmNlVft43gZhf91/6eQP/9uZ/3oHPcIWfX+vX6S4ze83MfunPqoXfPiv8f5/v7lejiIjsQWjvRURE8tqNwFIz+/Z+HHMscCSwE1gD3OycO8nMPgt8GvjHvRw/3cxe8tOLnXP/MLDDOfekmd2LN/PfXQB+rBfyr3E+cC1wNt6MVM3OuRPNLAosNrOHgEvxpmH9DzMLAoV4s85NdM4d7Z9zT0MnPmdmf+unG51zC4FfA5cD15pZFVDlnHvOzFLA6c65PjM7G/hP4G/2/s+X8VHn3E4ziwFLzOx3zrmvmNnVzrl5w5S/1P8cxwJl/jGP+fuOA44CNgGLgVPN7FXg3cARzjn3VoaLiIgMRwGyiIxpzrkWM7sd+AzQuY+HLXHObQYws9XAQ37+MmDhPhy/eg8B4Ju5218/D0zx0+8A5mb1dBcBM4ElwK1mFgbucc69ZGZrgGlm9kPggaw6D/XfzrmhPa2/8ctfixco35V1vZ+b2Uy8KdvD+/mZPmNm7/bT1X7dd7xJ+dOAO5xz/UCDmT0KnAi0AM865+oB/D8+pgBPA13ALf6Y6Lc8LlpEJJuGWIjI4eD7eL2x8ay8PvyfgWYWACJZ+7qz0ums7TQj17EwcI3+rGsY8Oms8cJTnXMPOeceA84ANgK3mdkHnXONeD2vi4BPAjfv64WdcxuBHWY2F7gCr0cZ4N+AR/xe6YuAgmEOz/w7+grAGxaB1wt+inPuWODFPRy/r7LbpB+vx70POAkvoL8Q+OMBnF9EJEMBsoiMec65nXi9pH+Xlb0OOMFPv4v97x09EK1Ach/K/Qn4lN9TjJnNMrO4mdUADc65n+IFwsf7DyIGnHO/A/4ZOH4/6/Rr4EtAkXNuqZ9XhBeEA3x4D8etG7iWmR0PTM06ttE512FmRwALso7pHfhMQzwOXOGPpy7H+yPg2T1V2MwSfn0fxBtnfuybfkIRkX2kAFlEDhf/hTeudcBPgTPN7GXgFKB9f05m3mvb/vUt1uVO4J/8h9/e7KG/m4EVwAtmthz4CV7vci3wspm9iNfjez0wEVjkDz/4BXDNHs75ORv8mrcpfv5dwJV4f0gM+DbwDf86e+o5/x1QYmavAFcDb/j5fwRC/jjhb+INhxhwE9648F8OOdfvgaXAy8BfgS8557bs4brg/ZFxv5ktBZ4APv8mZUVE9pk553JdBxERERGRUUM9yCIiIiIiWRQgi4iIiIhkUYAsIiIiIpJFAbKIiIiISBYFyCIiIiIiWRQgi4iIiIhkUYAsIiIiIpJFAbKIiIiISJb/D/PS6AupfaRmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAF9CAYAAAAZRJ4tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsnXlcVlX++N8HNEIwKwUTMcklZXsAgYRxAVJRcyNzybRAS9PvqFP2zRy/jKhpP1Ezx7QxI7c0zdJxd1JScyVxQQUclVFMRBENAQ1U4Pz+uA93HnYeFFE779frvni4Z/uc5d77Oed8zjlCSolCoVAoFAqFQqHQsKhpARQKhUKhUCgUiocJpSArFAqFQqFQKBQmKAVZoVAoFAqFQqEwQSnICoVCoVAoFAqFCUpBVigUCoVCoVAoTFAKskKhUCgUCoVCYYJSkBWKGkYIMVgIsb2m5ShECGEthNgkhMgUQnxf0/IoFA8SIUQrIUScECJbCDG2kmH2CyG8qls2cxBCJAshOj+AdCYLIVaYGUYIIZYIITKEEIeqUbYxQojI6opf8XijFGTFY4MQ4g0hxGEhxE0hxGUhxDYhRPualqsipJQrpZTBNS2HCf2AhkB9KWX/4o7GD+JdowKRLYQ4I4SYL4RoZOInUAhRYKwL08u/tASNH/Mco580IcRSIYRt9WXx/mCUc1pNy3E/EEK8KIT4Xghxzdg5OiGEGCeEsBRCOAkhZCn1OdAk/EtCiK1CiBtCiN+EEIeEEEONbn5CiB3G++nGdEzbixBCRAohrhuvSCGEMHFfJIQ4bWxTYeXk4SejnLXuoSjGA7uklHWllPNM4n5CCHFKCJFSLM1eQLaU8tg9pPlHoz3QBXCUUr5Ujel8BQwWQthXYxqKxxSlICseC4QQ44C5wCdoyt3zwBdAn5qUqyLu8UNeXTQFzkgp88rx852Usi7wLPAq8BxwxFTpAVKllLbFroPlxNlLSmkLtAF8gHBzBX9Iy7NMHhZ5hRDNgV+Ai4C7lLIe0B+tHuqaeH26WH1+ZwzvD+wEfgZaAPWBUUB3Y7hngEWAE1r7ygaWmMQ7AggBPAAD0At418T9OPA/wNFy8jAYqG1u3kuhKZBQyv0PgfRS7o8EvilHroeijsujBmRsCiRLKW9VZyJSylxgG/BWdaajeEyRUqpLXY/0BdQDbgL9y/FjhaZApxqvuYCV0S0QSEEbOboKXEb7WL8CnAF+AyaaxDUZ+AH4Du1DfxTwMHGfAPzH6JYIvGriFgbsBz4DrgPTjPf2Gd2F0e0qkAWcBNxM8rkc7SN9AU2BtDCJdx8wG8gAzgPdyykPZ2A3cANNGehtvD8FuAPcNZbp26WEnQysKHbPEk2JmW1apmbUYTLQ2eT/WcBm4++hwCljeZ4D3jXxV1h3HwFX0BSVZ4DNxnLKMP52NAmz21juB4x53ISm0K00lnks4GTivzWww9gOTgMDjPdHGMvpTmE8xvsOwFpj+ueBsaW0nRXGtN4BXgIOG/9PA+aUU07DgSSjLBsBBxM3iaasnTXW6wJAVLL8VwBbynF3MsZfqwz3fcACM+q7Ddqoa+H/B4ARJv+/DcSUkU5YGe+AM4BfeXJWQq6dQD6Qa6zTF433XzC2we6m7Rp4Asgp1r5Kq+Py3j8NjG30hrFe9/Lf57qitrQG7Z2QjfYc+xR7pv6K9g7KQOuQPFnWc1OJ9uXKf5+DNIzvREzeB2gdlFVGmZ8oo4zfNpZvvrGMp2DyDizWnlsYfy9Fa89bjHn9BWhekWxGt8FoMwI18n1S16N71bgA6lLXvV5ANyCvvI8iMBWIAewBO7QP8sdGt0Bj+EnGF/xw4wfpW7TRM1fjR/AFo//JaIpRP6P//zV+vGob3fsbP2wWwEDgFtDI6BZmTGsMUAuwpqiC3BU4AjyNpiw7m4RdDmwwyuSEphC8bRLvXaPslmijd6mUoiAZZU4CJqJ94F82fnRameRvRTllWaq7sYx/MSnTKinIQBO0j31h/fQAmhvLIwD4HWhTrO4i0ZQQazRl9zWgjrGsvgfWm6S125j/5miKVaKxLDsb62Q5sMTo1wZtVHWo0c0LuAa4GN2XAtNM4rYw1t8kY9k2Q1PquxZrOyFGv9bAQeBNo7st4FdGGb1sTLuNMa+fA3tM3CWaovU02gxKOtDN6PY8mgL2fBlxXwGGllM/TpSheBrLOR8IMqO+38NEAQYygbYm//tgokCb3C9LQV4AvF+enGbItht4p9i9zWgzJYEUVZBdgVulPB/F67i898//AxaiPZe1gQ5obb0ybSkXrSNvaYzHtEyTgXi05+lZtI75tHKemzLbF9pzdBn4AHjS+H9b0/eBMY4taM+EZQVlHIaJQlz8f5P2bKogX0frTNZC68yurkg2o3sb4Leqtgd1/XEvZWKheByoD1yT5ZsEDAamSimvSinT0UYt3jRxvwtMl1LeBVajjer8XUqZLaVMQFOiPEz8H5FS/mD0PwftxewHIKX8XkqZKqUskNoU9Fm0F3shqVLKz6WUeVLKnGJy3kV7wbdGU25PSSkvCyEsgdeBvxplSgY+LZaHC1LKr6SU+cAyoBGauUlx/NAUsRlSyjtSyp1oCsCgcsqvMqSifYgLcTDao5peNuWEXy+EuIGmBP2MZi6DlHKLlPI/UuNnYDuaElFIARAhpbwtpcyRUl6XUq6VUv4upcwGpqMp1qYsMcaZiTYF+x8pZbSxDX2PpggD9ESbCl5irK9jaKNjJWyzjfgCdlLKqcayPYdmB/m6iZ+DUsr1xvaRg1bnLYQQDaSUN6WUMWXEPRhYLKU8KqW8jTY66C+EcDLxM0NKeUNK+SuwC/A0luGvUsqnjfdLoz6aklER14rVpzPaiL1FJcMjhDCgKX0fmty2RVOSC8kEbE3tkMuJzwdoh6bQ3XeEEK+iKXz/LMX5abTOZXGK13F575+7aM9qUynlXSnlXimlpHJtaZ+Ucqvxmf+Gou8ogPlSyotSyt/QngPTZ7zIc0P57asncEVK+amUMtf4DvrFJK6ngH+hzZwNNcpzv/mnlPKQ8RldibFtV0K2bLSOsEJhFkpBVjwOXAcaVGBH54BmllDIBeM9PQ6Tl3qh0ppm4p6D9hEv5GLhDyllAdp0pQOAEOIt4yr4G0aFzw1N4S4RtjhGZXU+2ojYVePipKeM4WuXkofGJv9fMYnnd+PP0ha6OQAXjXKXFVdVaIw2xVlIqlEpM73KszkMMfppKqX8n8LOgxCiuxAixrjA6wbaiJlpeaZLzdYQo/86QogvhRAXhBBZwB7gaWMno5DidVtWXTcF2poqhWiKxHNl5KEpxToGaCP1ph2V4vX/NvAi8G8hRKwQomcZcRdpw1LKm2htv9Q2gDbSXtmFjtfRlLSKaFCsPk+hTd8XVCa8EKIFWofkL1LKvSZON9GUrEKeAm4aFcXy4rNAW2vwlwo6yIX+F5osMJxYCf82wEygrN0sMihqo11I8Tou7/0zC21GY7sQ4pwQYoLxfmXaUvH6frLYe9BUjuLvvCLPTXEZi7WvJmjKb1n4odmOz6iozu6Bstp2RbLVpWjnS6GoFEpBVjwOHARuo01plkUq2genkOeN96pKk8Ifxo+0I5AqhGiKNsozGm0XiKfRpjlNR8LK/YBIKedJKb0BFzTF6UO0qc+7peThUhVkTwWaGOW+17gAvQx6odlP3jeEEFZoI7azgYbG8txK+eX5AdAKbZr1KaBjYXRVEOEi8HMxpdBWSjmqjLQvAueL+a8rpXylLHmllGellIPQpt8jgR/KGGkv0oaNfupzD/VmQjSaWYrZGDtjBysKb3w2otFMC4ovakug6OinB6UvlCvOU2jmGN8JIa6g2Y8DpAghOhT3LKUcKf+7wPCTSsTfEs1sY68x/nVAIyHEFePIapKWNVG8c1m8XZT5/jGOeH4gpWwG9AbGCSE6Ubm2VBFNTH4Xf+eVK2Ox9nURzcSjLLajmXj8JIQobdaqIm6hmeoUpl1WB7Q0KpLNGW19hEJhFkpBVjzyGKfJJwELhBAhxhHE2saRx5lGb6uAcCGEnRCigdG/WXt3FsNbCNHXOFrzHpqCHoNmsyoxrnY3bnPlVtlIhRC+Qoi2QojaaB+NXKDAOLq9BpguhKhrVDbGVTEPv6CNwIw3llMgmnK72tyIhBC1jNPsq9BGVedUQZ7yeALNHjIdyBNCdAcq2hKvLtoo8A0hxLNAxD2kvxl4UQjxprGsahvryNnonkbRj/MhIFsI8ZHQ9pO2FEK4CSF8y0pACDFECGFnHNG/YbxdUIrXVcBQIYSnsePwCZrNd/I95K+QCOBPQohZhcqJEKKFEGKFEOLpSoQfD4QJIT4UQtQ3hvcQQqw2/m6MtgBuvpRyYSnhl6Mpho2FEA5onZylhY5C22LtSbROTm0hxJPGTlkm2sinp/EqVB690dr5vVJow1sY/ztode6JNgtzB03pL27CU5wy3z9CiJ7GshbG/OSj1b/ZbakU/iyEcDQ+B/+HtrC4PBnLal+b0ToG7wkhrIzvoLamgaWUM9HWbfxkzKM5HAdcjWk/iWbXXFkqki0AbdZCoTALpSArHguklJ+iKYzhaMrURbRR3PVGL9PQdgo4gbYzxFHjvaqyAW0BXgaaLWFfo/1gIppt8EG0D6k72uKYyvIU2gh0Btp053W0KVjQFvbdQluosw/tY7TYXMGNH/VeaCvyr6FNUb8lpfy3GdEMFELcRPugbzTK6S2lNB2hchAl9801a5RSajbEY9E6BxnAG8b0ymMu2oKha2idln+Zk2Yp6Qej2X2mok3zFi5sAvgacDFOga83dmR6oilQ540yRFG+DWQ3IMFYnn8HXpclbdORUkYDf0MbUb+Mtsjw9eL+SkMI8byx/J8vI5//AfzRRksThBCZxnQOU9TG9kax+hxnDH8AbZHXy8A5IcRvaNu6bTWGewetIzHZNLxJvF+i7SZyEk0p3WK8V8h2tE7Pn4zx5gAdpcaVwov/bsOWZmzn94TU7M5N4/8NrcN6xcQk60uKrgUojfLePy3RlOybaO+NL6SUu6rYlorzLVrZnUMzQyjznVde+zI+B13Q3htX0NZVBJUSx8do79xoo1JeKaSUZ9AWMkYb495nRtgyZTMq26+grclQKMxCVJ+5kELxeCKEmIy2unpITcuiUChqHiHEfmC0VIeFPFQIIcYATaSU42taFsWjx0O/gblCoVAoFA8zUsp2NS2DoiRSymrZ2UTxx0CZWCgUCoVCobivCCG2lWJiVandQxSKhwFlYqFQKBQKhUKhUJigRpAVCoVCoVAoFAoTlIKsUCgUCoVCoVCY8Egv0mvQoIF0cnJ64OneunULG5vyTsxVPKqoun18UXX7+KLq9vFG1e/jS03U7ZEjR65JKe0q8vdIK8hOTk4cPnz4gae7e/duAgMDH3i6iupH1e3ji6rbxxdVt483qn4fX2qiboUQFyr2pUwsFAqFQqFQKBSKIigFWaFQKBQKhUKhMOGhUpCFEDZCiMNCiJ41LYtCoVAoFAqF4o9JtdogCyEWo50lf1VK6WZyvxvwd8ASiJJSzjA6fQSsqU6ZFAqF4o/E3bt3SUlJITc3t6ZFqTbq1avHqVOnaloMRTWh6vfxpTrr9sknn8TR0ZHatWtXKXx1L9JbCswHlhfeEEJYAguALkAKECuE2Ag0BhKBJ6tZJoVCofjDkJKSQt26dXFyckIIUdPiVAvZ2dnUrVu3psVQVBOqfh9fqqtupZRcv36dlJQUXnjhhSrFUa0KspRyjxDCqdjtl4AkKeU5ACHEaqAPYAvYAC5AjhBiq5SyoDrlUygUised3Nzcx1o5VigUiuIIIahfvz7p6elVjqMmtnlrDFw0+T8FaCulHA0ghAgDrpWlHAshRgAjABo2bMju3burVdjSuHnzZo2kq6h+VN0+vvxR67ZevXrcvHmzpsWoVvLz88nOzq5pMRTVhKrfx5fqrtvc3Nwqv/cfun2QpZRLK3BfBCwC8PHxkTWxN6Lak/HxRdXt48sftW5PnTr12E9Pqyn4xxtVv48v1V23Tz75JF5eXlUKWxO7WFwCmpj872i8p1AoFIrHEEtLSzw9PXF1dcXDw4NPP/2UggJtknD37t3Uq1cPT09P/YqOji4Rh5OTE+7u7hgMBoKDg7ly5cqDzkapzJ07l99///2+xvnjjz/qZWFra0urVq3w9PTkrbfeuq/pFMfR0ZEbN26UuL969WqcnZ3p3LlzleNOTU1lwIABtGjRAm9vb3r06EFSUhJJSUlYW1sXqf+VK1cCkJWVxfDhwzEYDHh7exMUFERsbCwXLlwgMDAQFxcXXF1dmT9/vp7O9evX6dSpEy1btqRr165kZmYCkJCQgL+/P1ZWVsydO7eEfHl5eRgMBkJCQszOm2n5XL16lcDAQGxsbHjvvfeK+CsoKCAoKKjCGZ3CesjLy+Ppp58G4OLFiwwcONBs2cpi9+7duLq64unpyZ07d+5bvJ06ddLL/FGnJhTkWKClEOIFIcQTwOvAxhqQQ6FQKBQPAGtra+Li4khISGDHjh1s27aNKVOm6O4dOnQgLi5Ov8pSxHbt2sWJEyfw8fHhk08+qXT6+fn595yHsqiKglyRPF27dtXLwsfHh5UrVxIXF8fy5cvLDVddREVFsWTJklI7LqWRl5dX5H8pJSEhIQQHB5OUlMSRI0eYNm0aaWlpALRq1apI/Q8ePBiAYcOG8dxzz3H8+HGOHDlCVFQU165do3bt2sydO5fExEQOHjzIZ599xpkzZwCYPn063bt35+zZs3To0IGZM2cC0KBBAz7//HPef//9UmWeM2cObm5upbqZUz516tRh+vTpREZGlvC3adMmfHx8sLW1NTuNJk2a8N1331VJvtJYsWIFf/vb34iLi+OJJ564b/G+8cYbLFy48L7FV5NU9zZvq4BAoIEQIgWIkFJ+LYQYDfyIts3bYillQnXKoVAoFAqYsimBxNSs+xqni8NTRPRyrbR/e3t7Fi1ahK+vL5MnT65Smh07dmTevHkAjBo1il9++YXbt2/Tr18/XfF2cnJi4MCB7Nixg/Hjx5Odnc2iRYu4c+cOLVq04JtvvqFOnTqEhYVhbW3NsWPHuHr1KosXL2b58uUcPHiQtm3bsnTpUgC2b99OREQEt2/fpnnz5ixZsoTFixeTmppKUFAQDRo0YNeuXaX6s7W1LSHP1atXWbhwIbVq1cLFxYXVq1dXKu9RUVHEx8fro6DdunUjPDwcPz8/GjRowMiRI9m2bRt16tRhw4YN2Nvbk5aWxqhRo/j111+xsLBg3rx5+Pn5kZ6ezhtvvEFqairt27dHSlkivUmTJhETE0NoaCivvvoqERERjBw5kqNHj+qKaseOHYmKimLz5s1kZmZiYWHBTz/9pMexY8cObG1teeedd/R7hdPeSUlJpebz9OnTxMXFsWbNGm7dugVA8+bNad68OQAODg4APPXUU7Ru3ZpLly7x4osvsmHDBmJiYgAIDQ2lW7duTJ8+nYYNG9KwYUPWr19fIq0LFy7o9bJgwYJK1UNZ5TNjxgzatWtX6tZlK1euZOzYsfr/y5YtY8GCBdy5c4c//elPzJ8/HwuL0sctk5KS6NevH3FxcURFRbFlyxZ+++03UlNTCQ0NJTw8nOzsbAYMGEBqair5+flMnjyZfv36lYhr4cKFrFu3jp9++omtW7fy1ltvMX/+fL1sRo4cSfv27RkyZAiOjo688847bNiwgfz8fH744QdefPFFsrOzGT16NMeOHQNg6tSphISE0KdPHzp16sRHH31kVjk+jFT3LhaDyri/FdhanWlXGz+8TaM7DdH0foVCoVCYS7NmzcjPz+fq1asA7N27F09PT9197dq1uiJUGps3b8bd3R3QRgxr165NnTp16NSpEydOnMBgMABQv359jh49CmhT78OHDwcgPDycr7/+mjFjxgCQkZHBwYMH2bhxI71792b//v1ERUXh6+tLXFwcjo6OTJs2jejoaGxsbIiMjGTOnDlMmjSJOXPmsGvXLho0aMC1a9fK9FdcHgcHB86fP4+VlVWpZg1VITMzk4CAAGbMmMG4ceNYvHgxEyZMYOzYsYwfPx4/Pz+Sk5Pp2bMn8fHxREREEBQUxMSJE9mwYQOLFi0qEefUqVPZuXMn8+fPx9PTk8jISKysrDh58iQJCQm88sornD17FoBjx44RFxfHM888UySO+Ph4vL29y5T79OnTRer/iy++4MqVK3h5eZWpMBZy7tw54uPj8fX1BbR6trOzA6Bx48Zcvny5wnJ77733mDVrFteuXavQb3GKl095HDhwQO9wxcfH889//pMDBw5Qq1YtRowYwerVq3njjTcqle6hQ4eIj4/niSeewNfXl549e3L69GmcnJzYtm0bQJmmDiNHjmTfvn3069ePkJCQCmcGGjZsyLFjx5g3bx5z5sxh4cKFTJ48GTs7O06cOIGUUm/DDRo0IDs7mxs3bujmIY8qD90ivYeepGhs6reraSkUCoXCbMwZ6X2QdOjQgc2bN1foLygoCEtLSwwGA9OmTQNgzZo1LFy4kIKCAi5fvkxiYqKuIJvabMbHxxMeHs6NGze4efMmXbt21d169eqFEAJ3d3caNmyoK9+urq4kJyeTkpJCYmIi7dpp7/47d+7g7+9fQr6YmJhy/ZnKYzAYGDx4MCEhIVWyey0Na2trunfvDoC3tzd79+4FIDo6mtOnT+v+MjIyyMnJYc+ePWzdqo1V9enTp1KLpfbt28eHH34IaOXj4OCgjwIHBweXUI4rQ6GJhSnr1q2rMFxWVhavvfYan3/+eZXMFgDWr19PkyZNyrR9v59kZWVRp04dQKuT2NhYfHx8AMjJyaFJkyblBS9C165d9bIOCQlh3759dOrUiQkTJjBhwgR69eqlt8N7pW/fvoDWpgrbS3R0tD7iLIQoUu92dnZcvnxZKch/OCwsEWp7ZoVCoagy586dw9LSEnt7e7NO0SocqS3k/PnzzJ49m507d/L8888TFhZW5MRAGxsb/XdYWBjr16/Hw8ODpUuXFtn6ycrKCgALCwv9d+H/eXl5WFpa0qVLF1atWlWufFLKcv2ZyrNlyxb27NnDpk2bmD59OidPnqRWrYo/ybVq1dIXOAJF8mtqS2ppaanbAkspOXTo0H21NS0N0/yZ4urqWqkOUPEwcXFxRfJqyp07d+jbty9Dhw6ld+/e+v3CvW/t7Oy4dOkSjRo1KjedAwcOsG7dOjZu3Ehubi5ZWVmEhoaybNky3U9ycrLeiRk9enQRUxFzMB0Nl1IybNgwPv744yrFVXxfcyEEzs7OHD58mK1btzJhwgS6d+/OxIkTK4yrvDYF/30+TNtUeeTm5mJtbV2ZbDzU1MQivUcbYQGUtNNSKBQKRcWkp6czcuRIRo8efc+Hl2RlZWFjY0O9evVIS0vTp5ZLIzs7m0aNGnH37l19l4TK4ufnx/79+/WR0lu3bumLwurWravv41qeP1MKCgq4ePEiQUFBREZGkpmZWem9qp2cnDh27BhSSpKTkzly5EiFYTp37lzEtrZwtLZjx458++23gLaArDL70Xbo0EEvv1OnTnH58mVatGhRbpjg4GCysrJYvHixfu/48ePs37+/zDCtWrXC3d2dqVOn6rbR58+fZ9u2bUgpCQsLw9PTs4hNL0Dv3r115XbZsmX06dOnXNlmzpxJSkoKycnJrFixguDg4CLKMWhlXriAsKrKMUCLFi1ITk4GtDpZs2aNbtZx/fp1fv3110rHtX37dm7cuMHvv//Ohg0baNeuHZcuXcLW1pY333yTDz74QDfnqYimTZuSkJDAnTt3yMjIYOfOnRWG6dKli96mpJRkZGQA2gLUa9eu8fzzz1c6Lw8rSkE2F2GhRpAVCoXCDHJycvRt3jp37kxwcDARERG6e6ENcuH1ww8/VCpeDw8PvLy88Pb25o033ih3Svnjjz+mbdu2tGvXjtatW5slv52dHUuXLmXQoEEYDAb8/f3597//DcCIESPo1q0bQUFB5fozJT8/nyFDhuDu7o6Xlxdjx46t9HR0QEAAjRs3xtnZmQ8++KBCu1eABQsWsH//fgwGAy4uLnz11VcATJkyhejoaNzc3Ni8ebO+8K08xowZQ05ODu7u7gwePJjly5dXODIthGDDhg1s3bqV5s2b4+rqSnh4OM899xzwXxvkwqtQ8VqyZAkXL17Ew8MDNzc3hg0bhr29PT///DOrVq1ix44depgff/wRgIkTJ7JlyxZatmzJnj17dHOQlJQUHB0dmTdvHpMnT8bR0fG+b89XiKOjI+PHj+frr7/G0dFRN2/p0aOHPnPh7u5OREQEnTt31rcuLNzVozL4+vrSp08fPDw8GDRoEJ6enhw/fhxfX188PT355JNPKjV6DPDCCy8QEhKCq6srr7/+Om3atKkwTEREBGlpabi5ueHp6amb88TGxtK+ffsKbccfBURpq1YfdoQQvYBeLVq0GF64OOCB8akzl21caDRy7YNNV/FA+KMeJvFH4I9at6dOncLZ2bmmxahW1EESjzePS/2mpKTwzjvv8K9//eue4im+k8nDxJ///GcGDBhAQEBApfxXd92W9v4TQhyRUvpUFPaRVPGllJuklCPq1av34BMXFoAaQVYoFAqFQlF5HB0dCQsLe6yPfvfy8qq0cvywoxbpmYuFBeIRHHVXKBQKhUJRs7z++uv3HIc5dtC9e/cuYds8e/bsezoVsTzuxUb7YUMpyOaiFukpFAqFQqF4BNi4UR1UXFUeSROLGkUt0lMoFAqFQqF4rFEKsrkItQ+yQqFQKBQKxeOMUpDNRS3SUygUCoVCoXisUQqyuQi1SE+hUCgUCoXicUYpyOZiYYkaQVYoFIrKY2lpqR8U4uHhwaeffqofbbt7927q1atX5KCI6OjoEnE4OTnh7u6uH6pw5cqVB52NUpk7d+59P3Dixx9/1MvC1taWVq1a4enpyVtvvXVf0ymOo6MjN27cKHF/9erVODs739POB6mpqQwYMIAWLVrg7e1Njx49SEpKIikpCWtr6yL1X3hSX1ZWFsOHD8dgMODt7U1QUBCxsbFcuHCBwMBAXFxccHV1Zf78+Xo6169fp1OnTrRs2ZKuXbt43rdZAAAgAElEQVSSmZkJQEJCAv7+/lhZWZW6f3BeXh4Gg0E/UtocTMvnX//6F23atMHd3R1vb+8iR5oXFBQQFBTEzZs3SUpKqtQhL5Xh6NGjVd5buWvXrpU6QbGq5OTk8PLLL5t1AFBlmDt3LsuXL79v8ZWKlPKRvby9veUD5x/tZPq8Tg8+XcUDYdeuXTUtgqKa+KPWbWJiYk2LIG1sbPTfaWlpslOnTnLSpElSSq1eevToUWEcTZs2lenp6VJKKf/617/KMWPG6G5ZWVnlhs3Ly6uK2JXCVK7KYo48AQEBMjY21lyxqkTjxo1lRkZGifudOnWSBw8erHQ8d+/eLfJ/QUGB9PX1lV999ZV+7+jRo3Lfvn3y7Nmz0sPDo9R4XnvtNRkeHi4zMzOllFImJSXJrVu3ykuXLsljx45JKaXMzMyUzZo1k6dPn5ZSSvn+++/LWbNmSSml/Pjjj+XEiROllFJeuXJFxsbGyo8++kh+9tlnJdKKjIyUgwYNkn369Kl0PgsxLZ8jR47I1NRUKaWUcXFx0tHRUfe3fv16+b//+79SSlluvs3lq6++kn/5y1/uS1xVpXidF7J3717ZtWvXMsNV9OyWRXZ2tmzTpk2F/kp7/wGHZSV0TDWCbC7CUplYKBSKR5NtE2BJj/t7bZtglgj29vYsWrSI+fPnI6v4Lu3YsSNJSUkAjBo1ioCAAFxdXYscX+3k5MRHH31EmzZt+P777/nqq6/w9fXFw8OD1157TR/1DQsLY9SoUfj5+dGsWTN2797NsGHDcHZ2JiwsTI9v+/bt+Pv706ZNG/r378/NmzeZN28eqampBAUFERQUVKa/0uSZN28eLi4uGAwGs/bGjYqK4r333tP/79atG/v27SMvL4+nn36aCRMm4OHhgb+/P1evXgUgLS2Nvn374uPjw0svvURMTAwA6enpdOnSBVdXV959991S62PSpEnExMQQGhrKhAkTyMnJITQ0FHd3d9q0acOePXt0uUJCQggKCqJr165F4tixYwe2trZF9sj18vIq92jw06dPExcXx5QpUxBCANC8eXO6d++Og4ODPvr61FNP0bp1ay5dugTAhg0bCA0NBSA0NJT169cD0LBhQ3x8fKhVq+TuthcuXGDHjh0MHTq0vKIvleLl06ZNGxo1agRox0nfvHmTu3fvArBy5Ur69Omjh7179y6vv/46zs7ODBgwgJycHEA7rjkgIABvb2+6d++uH0Hdvn17JkyYwEsvvUSrVq04cOAAOTk5TJ06lZUrV5Y7SpudnU1oaCgGgwGDwaCXi+msQUREBK1ataJDhw4MHDhQH2lfuHCh/uz0799fl3PIkCGMGjWKl156qdRjrVNTUwkLC+PgwYN4enqSnJxcJL2YmBh69+4NQHh4OG+//TYBAQE0a9ZMP24ctCPHDQYDHh4eeh3Z2tri4ODA0aNHza6zyvJIKshCiF5CiEWFUycPNnG1D7JCoVDcC82aNSM/P19X4Pbu3Vtkiv0///lPueE3b96Mu7s7ANOnT+fnn3/mxIkT+t9C6tevz9GjR3n99dfp27cvsbGxHD9+HGdnZ77++mvdX0ZGBgcPHuSzzz6jd+/evP/++yQkJHDy5Eni4uK4du0a06ZNIzo6mqNHj+Lj48OcOXMYO3YsDg4O7Nq1i127dpXprzR5ZsyYwbFjxzhx4gQLFy68L+WamZlJQEAAx48fx9/fn8WLFwMwduxYxo8fz+HDh1mzZo2uqEZERBAUFERCQgKvvPIKqampJeKcOnUqnp6efPfdd8yYMYN58+ZhZWXFyZMn+eabb3jzzTe5c+cOAMeOHWPdunX89NNPReKIj4/H29u7TLlPnz5dpP4PHDhAQkICXl5eWFiUr6acO3eO+Ph4fH19Ac3Ews7ODoDGjRtz+fLlCsvtvffeY9asWboibg7Fy8eUNWvW0LZtW2rXrg3AgQMHaNOmje6emJjIe++9x6lTp3jyySf58ssvuX37Nn/5y19Yu3YtR44cYciQIfztb3/Tw0gpOXToELNmzWLq1KlYW1szadIkBg8eTFxcHP369StVzsmTJ2NnZ8eJEyc4fvx4idPuYmJi2Lx5MydOnGDLli3Exsbqbv3799efnebNm7N06VLd7fLly8TExDBz5swSaTo4OLBw4UKCgoKIi4vDycmp3LI8c+YMO3bsICYmhkmTJpGfn8/x48eJjIxk9+7dHD9+nE8//VT37+Pjw969e8uN8154JA8KkVJuAjb5+PgMf9BpZyXdJu+JB52qQqFQ3Ae6z6jYTw3QoUMHNm/eXKG/oKAgLC0tMRgMTJs2DdCUkIULF1JQUMDly5dJTEzEYDAAMHDgQD1sfHw84eHh3Lhxg5s3bxYZ5ezVqxdCCNzd3WnYsKGufLu6upKcnExKSgqJiYn6iOedO3fw9/cvIV9MTEy5/kzlMRgMDB48mJCQkCrZvZaGtbU13bt3B8Db21tXHqKjozl9+rTuLyMjg5ycHPbs2cPWrVsB6NOnD3Xr1q0wjX379vHhhx8CWvk4ODjoo/nBwcE888wzZsvdqlUr4uLiitxbt25dheGysrJ47bXX+Pzzz7G1tTU7XYD169fTpEmTMm3fq8rJkycJDw9nx44d+r2srCzq1Kmj///CCy/g5+cHaKOxixYtIjAwkISEBN3eOz8/H0dHRz1M3759Aa1+k5OTKy1PdHS0PmoshChRT/v27SMkJAQrKyusrKzo2bOn7nbixAkmTZrEjRs3yM7OLuLWv3//CjsxlaVnz5488cQT2Nvb8+yzz5Kens7OnTsZOHAgzz77LID+F7TZKHPKwFweSQW5Jrm8PROrllY1LYZCoVA8spw7dw5LS0vs7e05depUpcPt2rWLBg0a6P+fP3+e2bNns3PnTp5//nnCwsLIzc3V3W1sbPTfYWFhrF+/Hg8PD5YuXVpk8ZSVlfZOt7Cw0H8X/p+Xl4elpSVdunRh1apV5conpSzXn6k8W7ZsYc+ePWzatInp06dz8uTJUqf/i1OrVi19gSNQJL9PPPHf0RtLS0vy8vJ0uQ4dOlTEvTowzZ8prq6uleoAFQ8TFxdXJK+m3Llzh759+zJ06FB9mh60Ufr09HTs7Oy4dOmSbu5QFgcOHGDdunVs3LiR3NxcsrKyCA0NZdmyZbqf5ORkvRMzevToCo9T/vXXX+nbty8rVqzghRde0O8XVySLj1gLIZBSYjAYyhwZLWyfpvVb3bz11lts27YNNzc3oqKidBMdKLvOy8K0/Zq2XaDIs1eZ/OXm5mJtbW1W+ubwSJpY1CTCAshXu1goFApFVUhPT2fkyJGMHj26SlPapmRlZWFjY0O9evVIS0tj27ZtZfrNzs6mUaNG3L17V98lobL4+fmxf/9+faT01q1bnDlzBoC6devquwCU58+UgoICLl68SFBQEJGRkWRmZuq2yhXh5OTEsWPHkFKSnJzMkSNHKgzTuXPnIjadhaO1HTt25NtvvwVg06ZNldrNoEOHDnr5nTp1isuXL9OiRYtywwQHB5OVlaWbfAAcP36c/fv3lxmmVatWuLu7M3XqVN02+vz582zbtg0pJWFhYXh6ejJ27Ngi4Xr37q0rt8uWLSti81saM2fOJCUlheTkZFasWEFwcHAR5Ri0Mo+LiyMuLq5C5TgjI4MePXowe/ZsfXS4kBYtWhQZ8Tx//rxuyvDtt9/Svn17XFxcuHTpEocOHQK0jkBCQkK5aZq2wbLo0qWL3gaklGRkZBRxb9euHRs3buT27dtkZ2frMwugtePnnnuOu3fv6u2lqjg5Oeltdu3atRX6f/nll/nuu+/47bffAPS/oJlkuLm53ZM85aEUZDMRFgLylQ2yQqFQVJacnBx9m7fOnTsTHBxcZEFdcRvkym4H5eHhgZeXF97e3rzxxhvlLvr6+OOPadu2Le3ataN169ZmyW9nZ8fSpUsZNGgQBoMBf39//v3vfwMwYsQIunXrRlBQULn+TMnPz2fIkCG4u7vj5eXF2LFjefrppyslS0BAAI0bN8bZ2ZkPPvigUluFLViwgP3792MwGHBxceGrr74CYMqUKURHR+Pm5sbmzZtxcHCoMK4xY8aQk5ODu7s7gwcPZvny5RWOTAsh2LBhA1u3bqV58+a4uroSHh7Oc889B5S0QS5U5JYsWcLFixfx8PDAzc2NYcOGYW9vz88//8yqVavYsWOHHubHH38EYOLEiWzZsoWWLVuyZ88e3RwkJSUFR0dH5s2bx+TJk3F0dLzv2/MB/P3vf+f8+fNERETosl2/fh2AHj16FJm5cHZ2Zs6cOTg7O/P7778zYsQIrKys+OGHHxg3bhwGgwEvLy9++eWXctN8+eWXOX78OF5eXmU+OxEREaSlpeHm5oanp2eJEWp/f3+6deuGu7s7r7zyCu7u7tSrVw/Q7Kx9fX1p164dLi4u91A6mi30//zP/+Dr61upGQ0PDw/Gjx9Px44d8fT01OsT4ODBg3Tq1Ome5CkPUdVVxA8DPj4+8vDhww80zaS27tSyFzhtOlGxZ8Ujx+7duwkMDKxpMRTVwB+1bk+dOoWzs3NNi1GtZGdnV8p+VvFo8rjUb0pKCu+8806V9yyubm7evImtrS23bt2iffv2LFu2TLfnry6qWrexsbF88cUXLFmypFx/pb3/hBBHpJQ+FaWhbJDNRFgKpBpBVigUCoVCYQaOjo6EhYXpiujDxttvv83p06fJzc1l2LBh1a4c3wu//fYbU6ZMqdY0lIJsJsJSQIFSkBUKhUKhUJiHOXteV4WoqKgiJwuCZms+b968CsN+9913NZJuVSi+13Z1oBRkc7G0gIL8mpZCoVAoFAqFogjvvPNOhQsJH6d0qxO1SM9MhIVAqhFkhUKhUCgUiscWpSCbiWZiUdNSKBQKhUKhUCiqC6Ugm4mwtFDbvCkUCoVCoVA8xjySCrIQopcQYlFmZuaDT9vSAqlGkBUKhUKhUCgeWx5JBVlKuUlKOaJwE+sHibBUNsgKhUJhDpaWlvpBIR4eHnz66af6cbO7d++mXr16RQ6KiI6OLhGHk5MT7u7uGAwGgoODuXLlyoPORqnMnTv3vh848eOPP+plYWtrS6tWrfD09OStt966r+kUx9HRkRs3bpS4v3r1apydnencuXOV405NTWXAgAG0aNECb29vevToQVJSEklJSVhbWxep/8KT+rKyshg+fDgGgwFvb2+CgoKIjY3lwoULBAYG4uLigqura5HdE65fv06nTp1o2bIlXbt2pXAgLSEhAX9/f6ysrJg7d24J+fLy8jAYDPqR0uZgWj4HDx7Ew8MDT09PPDw82LBhg+7v1q1bBAYGlnp89pAhQ1i/fj0AQ4cO5fTp02bLca+kpaXh6+uLl5cXBw4cuG/xvv/+++zZs+e+xffAkFI+spe3t7d80FwM8Zdn27Z+4OkqHgy7du2qaREU1cQftW4TExNrWgRpY2Oj/05LS5OdOnWSkyZNklJq9dKjR48K42jatKlMT0+XUkr517/+VY4ZM0Z3y8rKKjdsXl5eVcSuFKZyVRZz5AkICJCxsbHmilUlGjduLDMyMkrc79Spkzx48GCl47l7926R/wsKCqSvr6/86quv9HtHjx6V+/btk2fPnpUeHh6lxvPaa6/J8PBwmZmZKaWUMikpSW7dulVeunRJHjt2TEopZWZmpmzWrJk8ffq0lFLK999/X86aNUtKKeXHH38sJ06cKKWU8sqVKzI2NlZ+9NFH8rPPPiuRVmRkpBw0aJDs06dPpfNZiGn53Lp1S8//pUuXpL29vczPz5dSSjl37lw5f/78UuMYPHiw/Oc//2l22veTb775Rr777rv3Pd6kpCTZrVu3Ut0qenbvldLef8BhWQkdU23zZi6WFjzChw8qFIo/MJGHIvn3byWPPr4XWj/bmo9e+qjS/u3t7Vm0aBG+vr5Mnjy5Smma7q86atQofvnlF27fvk2/fv30wwOcnJwYOHAgO3bsYPz48WRnZ7No0SLu3LlDixYt+Oabb6hTpw5hYWFYW1tz7Ngxrl69yuLFi1m+fDkHDx6kbdu2LF26FIDt27cTERHB7du3ad68OUuWLGHx4sWkpqYSFBREgwYN2LVrV6n+bG1tS8hz9epVFi5cSK1atXBxcWH16tWVyntUVBTx8fH6KGi3bt0IDw/Hz8+PBg0aMHLkSLZt20adOnXYsGED9vb2pKWlMWrUKH799VcsLCyYN28efn5+pKen88Ybb5Camkr79u2RpXzcJk2aRExMDKGhobz66qtEREQwcuRIjh49Su3atZk7dy4dO3YkKiqKzZs3k5mZiYWFBT/99JMex44dO7C1tS2yDZiXlxcASUlJpebz9OnTxMXFsWbNGm7dugVA8+bNad68OYB+LPZTTz1F69atuXTpEi+++CIbNmwgJiYGgNDQULp168b06dNp2LAhDRs21EdpTblw4YJeL4XHXFeW4uUzY8YM3S0nJwdAL9eVK1eybt06AAoKChg9ejQ7d+6kSZMmWFpa6uHat2/P/PnzcXNzY+jQocTFxSGlZMSIEYwdO5YzZ84wcuRIrl+/jqWlJevWrSMpKYn58+fr+Rs5ciTt27dnyJAhfPjhh2zZsoVatWrRvXt3IiMjS+Tj8OHDTJw4kZycHGJiYjh06BD29vb6jMLq1auJjo4mKiqKIUOGUL9+fWJjY7ly5Qqffvopr776KgCffPIJq1atwsLCgp49ezJ9+nSaN2/O5cuXSU9Px87OzqzyrUmUgmwmwtJCHRSiUCgU90CzZs3Iz8/n6tWrAOzduxdPT0/dfe3atboiVBqbN2/G3d0dgOnTp1O7dm3q1KlDp06dOHHihH4CWP369Tl69CigTb0PHz4cgPDwcL7++mvGjBkDQEZGBgcPHmTjxo307t2b/fv3ExUVha+vL3FxcTg6OjJt2jSio6OxsbEhMjKSOXPmMGnSJObMmcOuXbto0KAB165dK9NfcXkcHBw4f/48VlZWpZo1VIXMzEwCAgKYMWMG48aNY/HixUyYMIGxY8cyfvx4/Pz8SE5OpmfPnsTHxxMREUFQUBATJ05kw4YNLFq0qEScU6dOZefOncyfPx9PT08iIyOxsrLi5MmTJCQk8Morr3D27FkAjh07RlxcHM8880yROOLj4/H29i5T7tOnTxep/y+++IIrV67g5eWFhUX5lqDnzp0jPj4eX19fQKvnQiWscePGXL58ucJye++995g1axbXrl2r0G9xipcPwIEDBxg+fDgXLlzg22+/xdLSktzcXFJSUnB0dATghx9+4Pz58yQmJpKamoqLiwsjR44sEveRI0e4du0aJ0+eBNDbyaBBg5g8eTK9evUiNzeXgoKCMjsaaWlpbN26lYSEBIQQZbY1Hx8fJk2apHe+8vLyys331atX2b9/PydPnmTAgAG8+uqrbNq0iW3btnHo0CGsra357bffdP+FZht9+vSpRKk+HCgF2UxELUukOidEoVA8gpgz0vsg6dChA5s3b67QX1BQEJaWlhgMBqZNmwbAmjVrWLhwIQUFBVy+fJnExERdQR44cKAeNj4+nvDwcG7cuMHNmzeLnMTVq1cvhBC4u7vTsGFDXfl2dXUlOTmZlJQUEhMTadeuHQB37tzB39+/hHwxMTHl+jOVx2AwMHjwYEJCQqpk91oa1tbWdO/eHQBvb2/27t0LQHR0dBGb1oyMDHJyctizZw9bt24FoE+fPtStW7fCNPbt28eHH34IaOXj4OCgK2fBwcEllOPK0KpVK+Li4orcKxxpLY+srCxee+01Pv/88yof3bx+/XqaNGlSpu17VfjTn/5EQkICCQkJDBs2jG7dunH16lWeffZZ3c+ePXsYNGgQFhYWODo6EhgYWCKeFi1acPr0acaOHUuPHj0IDg4mIyODa9eu0atXLwCefPLJcmV59tlnsbCwYPjw4fTo0YOePXvelzyGhIQghMBgMHDp0iVAa2fDhg3D2tpaT7sQe3t7UlNT70vaDwqlIJuJNoJc01IoFArFo8u5c+ewtLTE3t6eU6dOVTpc4UhtIefPn2f27Nns3LmT559/nrCwMHJzc3V3Gxsb/XdYWBjr16/Hw8ODpUuXsnv3bt3NysoKAAsLC/134f95eXlYWlrSpUsXVq1aVa58Uspy/ZnKs2XLFvbs2cOmTZuYPn06J0+epFatij/JtWrVKrLIyzS/TzzxhP7b0tJSHwWUUnLo0KEi7tWBaf5McXV1rVQHqHiYuLi4Uhe0gdb56Nu3L0OHDqV37976/fr16+tT+ZcuXaJRo0blpnPgwAHWrVvHxo0byc3NJSsri9DQUJYtW6b7SU5O1jsxo0ePrvSJca6urlhZWZGYmEjjxo2L1FVlqF+/PidOnGDbtm0sWLCAtWvXlmoeAWW3i9q1a3P48GF27NjB999/zz/+8Q+2b99eYdoWFhZFTG6Ky276nJRmmlOc3NxcXXF+VHgkd7GoSdQ2bwqFQlF10tPTGTlyJKNHj0YIcU9xZWVlYWNjQ7169UhLS2Pbtm1l+s3OzqZRo0bcvXtX3yWhsvj5+bF//359pPTWrVucOXMGgLp165KdnV2hP1MKCgq4ePEiQUFBREZGkpmZyc2bNysli5OTE8eOHUNKSXJyMkeOHKkwTOfOnYvY1haO1nbs2JFvv/0WgE2bNun5KI8OHTro5Xfq1CkuX75MixYtyg0THBxMVlYWixcv1u8dP36c/fv3lxmmVatWuLu7M3XqVF0BO3/+PNu2bUNKSVhYGJ6enowdO7ZIuN69e+vK7bJlyyqc0p85cyYpKSkkJyezYsUKgoODiyjHoJV5XFwccXFxFSrH58+fJz8/X/999uxZmjZtip2dHTk5Ody5cwfQyv67776joKCAS5cu8fPPP5eIKz09HSkl/fv3Z+rUqRw9epRnnnkGOzs7Nm3aBGiK5++//07Tpk1JSEjgzp07ZGRksHPnTkBr91lZWfTs2ZPPPvuMY8eOlSt/IRYWFjzzzDOcPXuWgoIC/vnPf1YYpkuXLixevFi3vTY1sThz5gxubm6VSvthQY0gm4mopRRkhUKhMIecnBw8PT25e/cutWrV4s0332TcuHG6e3Eb5PDwcPr161dhvB4eHnh5eeHt7U3Tpk1104bS+Pjjj2nbti12dna0bdu2UspgIXZ2dixdupRBgwZx+/ZtAKZNm8aLL77IiBEj6NatGw4ODuzatatMf6bk5+czZMgQMjMzkVIyduxYnn766UrJEhAQQOPGjXF2dsbV1bVIuZXFggULGDVqFEuWLCEvL4+goCAWLFjAlClTGDRoECtWrKBdu3b6wrfyGDNmDO+++y7u7u7Url2b5cuXVzgyLYRgw4YN/OUvf2H69Ok8+eSTNGvWjLlz5yKlLGGDPHz4cP785z+zZMkSxo0bh4eHBzY2NtjZ2TF79mx+/vlnVq1ahcFg0MNFRkbStWtXJk6cyIABA/jyyy954YUX+O677wBISUnBz8+PrKwsLCwsmD17NmfOnKFOnToV5tkcfv75Z2bNmkXt2rWxtLTkyy+/1M1OOnfuzIEDBwgMDKRfv37s2rULFxcXnn/++VJNdi5evMjbb7+NlBIhhD56vHLlSt59913+7//+jyeeeIK1a9fywgsvEBISgqurK82aNaNNmzaAZpfet29fbt++TUFBAXPmzKl0XgrL1N7eHm9vb71Nl0XPnj05fvw4Pj4+1K5dm169evHxxx9z+/ZtkpOT9YWZjwqiMkPjDys+Pj7y8OHDDzTNqyO789ue87ROvL8rwRUPB7t37y7VFkzx6PNHrdtTp07h7Oxc02JUK9nZ2ZWyn1U8mjwu9RsbG8sXX3zBkiVLalqUB8r3339PYmIiERERJdyqu25Le/8JIY5IKX0qCqtMLMyllqXa5k2hUCgUCoVZ+Pr60r59+zLtqh9XpJS8//77NS2G2SgTCzMRlpYgBTI/X/utUCgUCoVCUQnefvvtmhaBqVOnltgl5PXXX2fChAnVkt6AAQOqJd7qRinIZiJqaUqxzMtTCrJCoVAoFIpHikmTJul7cyvKRplYmEmhUizvlr+JtkKhUCgUCoXi0UQpyGYiLI1Flq8UZIVCoVAoFIrHkUdSQRZC9BJCLMrMzHzwiReaWOSr4/QUCoVCoVAoHkceSQVZSrlJSjmiXr16DzztwhFkWcE55QqFQqFQKBSKR5NHUkGuSYSlcV2jGkFWKBSKSmFpaYmnpyeurq54eHjw6aef6ltd7d69m3r16uHp6alf0dHRJeJwcnLC3d0dg8FAcHAwV65cedDZKJW5c+fy+++/39c4f/zxR70sbG1tadWqFZ6enrz11lv3NZ3iODo6cuPGjRL3V69ejbOzM507d65y3KmpqQwYMIAWLVrg7e1Njx49SEpKIikpCWtr6yL1X3hSX1ZWFsOHD8dgMODt7U1QUBCxsbFcuHCBwMBAXFxccHV1Zf78+Xo6169fp1OnTrRs2ZKuXbtSONOckJCAv78/VlZWzJ07t4R8eXl5GAwG/UhpcyitfM6fP4+NjU2RtG7dukVgYGCNbPMWFRXFe++9d9/jDQ8PL7U8SyMxMVE/3Cc5Ofm+ydC/f3/OnTt33+IrRCnI5lJLU5Dl3bs1LIhCoVA8GlhbWxMXF0dCQgI7duxg27ZtTJkyRXfv0KGDfpRvXFxcmYrYrl27OHHiBD4+PnzyySeVTj+/Ggc0qqIgVyRP165d9bLw8fFh5cqVxMXFsXz58nsRtcpERUWxZMmSUjsupZFXbIZVSklISAjBwcEkJSVx5MgRpk2bRlpaGqAdK21a/4MHDwZg2LBhPPfccxw/fpwjR44QFRXFtWvXqF27NnPnziUxMZGDBw/y2Wef6Ud6T58+ne7du3P27Fk6dOjAzJkzAWjQoAGff/55mfvxzpkzp8pHIZdWPuPGjaN79+4l/PXv3x8Li6KqV/HyqimklNWqvK9bt45BgwZx7NgxnJyc7pikCcwAACAASURBVFu8I0eOZNasWfctvkKUgmwmolZt7cednJoVRKFQKMzkyiefcOHNt+7rdcUMRRXA3t6eRYsWMX/+fKp6kmvHjh1JSkoCYNSoUQQEBODq6lrkpC4nJyc++ugj2rRpw/fff89XX32Fr68vHh4evPbaa7pSGxYWxqhRo/Dz86NZs2bs3r2bYcOG4ezsTFhYmB7f9u3b8ff3p02bNvTv35+bN28yb948UlNTCQoKIigoqEx/pckzb948XFxcMBgMvP7665XOe/GRwG7durFv3z7y8vJ4+umnmTBhAh4eHvj7+3P16lUA0tLS6Nu3Lz4+Prz00kvExMQAkJ6eTpcuXXB1deXdd98ttT4mTZpETEwMoaGhTJgwgZycHEJDQ3F3d6dNmzbs2bNHlyskJISgoCC6du1aJI4dO3Zga2vLO++8o9/z8vIq92jw06dPExcXx5QpUxBCANC8eXO6d++Og4ODfsT0U089RevWrbl06RIAGzZsIDQ0FIDQ0FDWr18PQMOGDfHx8aFWrZK72164cIEdO3YwdOjQ8oq+VIqXD8APP/xA69atad26dRG/K1eupE+fPgBER0cTGBhIz549cXd3B2DmzJm4ubnh5ubG559/DmgnzXXv3h0PDw/c3Nz44YcfAO1UvoCAALy9venevbve2Wjfvj0TJkzgpZdeolWrVhw4cKBIPgMCAmjZsiXTpk0DICkpCRcXFwYPHoyrqyuXL19mxYoVuLu74+bmxsSJE/XwW7ZsoU2bNnh4eBAcHFyiLP7xj3/Qo0cPcnNzS7ht3LiR+fPn8/nnn9O5c2eSkpKKHC8+Y8YMXaay8pCXl8f777+Pm5sbBoOBL774AoDAwED+9a9/3feOsNoH2UxEbe3MeakUZIVCoagSzZo1Iz8/X1fg9u7dW+RjuXbtWpo3b15m+M2bN+tKxfTp06lduzZ16tShU6dOnDhxAoPBAED9+vU5evQooE29Dx8+HNCmhb/++mvGjBkDQEZGBgcPHmTjxo307t2b/fv3ExUVha+vL3FxcTg6OjJt2jSio6OxsbEhMjKSOXPmMGnSJObMmcOuXbto0KAB165dK9NfcXkcHBw4f/48VlZWpZo1VIXMzEwCAgKYMWMG48aNY/HixUyYMIGxY8cyfvx4/Pz8SE5OpmfPnsTHxxMREUFQUBATJ05kw4YNLFq0qEScU6dOZefOncyfPx9PT0/+P3v3HR5lme9//H3PpBcSSKGF3oSQhC6oCFFAEUGOuq5tD1jWchbLusezrj/7qse2ysG1rA27LrqoNBsKolgAKRKIKCVA6CW9J3P//phkNgGSTCCTScLndV25knnyPPN8p4ifufN97vvRRx8lODiY9evXs2HDBs477zx+/fVXANasWcPatWtp27ZtjftIS0tj6NChtda9adOmGq//s88+y969exk8ePBRo61H2rp1K2lpaQwfPhxwv85xcXEAdO7cmT179tT7vN166608/vjjHDx4sN59j3Tk85OXl8ff/vY3vvjiixp/5SguLiYzM5OEhATPtlWrVrFx40a6du3KDz/8wFtvvcXKlSspLy9nxIgRjB07lo0bN9K9e3c+/vhjwP0al5SUcMsttzBv3jxiY2N56623uPvuuz2vn7WWFStWMG/ePB544AE++eQTAFasWEFaWhpBQUEMHz6c888/n4iICH7++Wdef/11hg0bRmZmJnfddRerVq0iKiqKcePGsWDBAoYNG8aNN97I119/Tbdu3Th8+HCN52HmzJl89dVXfPDBBwQFBR31PE2ZMoUVK1YQGxvLrbfe6vmAW5tjPYbnnnuO3bt3s27dOpxOp6cGp9NJ9+7dSUtLIyUlpcGvYW0UkBvKE5BL/FyIiEjDdKg2GtScjB49mgULFtS7X2pqKk6nk+TkZM9o05w5c3j++edxuVzs2bOHjRs3egLyb3/7W8+xaWlp3HXXXWRnZ5Ofn19jlHPy5MkYY0hKSqJ9+/ae8J2YmEhGRgaZmZls3LjRM+JZWlrKqFGjjqrv+++/r3O/6vUkJydzxRVXMHXq1OPqez2W0NBQz5/1hw4dytdffw24Rys3bdrk2S8rK4uioiKWLVvGokWLALjggguIjIys9xzffPMNt99+O+B+fjp16uQJOxMmTDgqHHujqsWiuiNXejuW3NxcLrroIp5++mkiIiIafF6ADz/8kC5dutTa+95Qd999N7fffjthYWE1tu/fv5927drV2DZq1Ci6du0KuJ/Xiy66iNDQUACmTp3K119/TWpqKnfccQd33HEHkydP5vTTT/e0K1W1IlVUVNQI3hdeeCHgfg9U7/U955xzPK/P1KlT+eabbzj33HPp1asXw4YNA+CHH37grLPOIjY2FoDLL7+cZcuWUVZWRmpqKt26dQOo8Vhmz55Nt27dmDt37jFH6I/HsR7D4sWLufXWW3FWrkdRvYb4+Hh2796tgOxPJqAyIJdoBFlE5Hhs3boVp9NJfHw86enpXh9XNVJbZdu2bTzxxBN8+eWXdO3alenTp9f48254eLjn5+nTp/Phhx+SkpLCq6++ytKlSz2/Cw4OBsDhcHh+rrpdXl6O0+lk/PjxvPPOO3XWZ62tc7/q9SxcuJBly5Yxf/58HnroIdavX+9VuAgICKjRJ1r98VYfuXM6nZ7e1qrRuGON7DWm6o+vusTERK8+AB15zNq1a2vtiS0tLeXCCy/kqquuYsqUKZ7tMTExHDhwgLi4OHbt2kXHjh3rPM+3337L3LlzmTdvHsXFxeTm5jJt2jRee+01zz4ZGRmeDzEzZsyo0SpypBUrVvDhhx9y2223kZ2d7XlPXXzxxUe1HtT2fFXXv39/Vq1axaJFi7jjjjuYOHEiEydOJDk52fMB6EhV7+Hq7wHA06py5G1v6qhLUlISa9asYdeuXZ4AXZ9jvY+rv/9rewy1KS4u9ny4aCzqQW6oyhFkyhSQRUQa6sCBA9xwww3MmDHjqP9hN1Rubi7h4eFERUWxb98+z5+hjyUvL4+OHTtSVlbmmSXBWyNHjmT58uWekdKCggLPRWGRkZHk5eXVu191LpeLnTt3kpqayqOPPkpOTo6nV7k+3bt3Z82aNVhrycjI4Mcff6z3mHHjxvHMM894bleN1p555pm8/fbbAMyfP9/zOOoyevRoz/OXnp7Onj176N27d53HTJgwgdzcXF555RXPtnXr1rF8+fJaj+nXrx9JSUk88MADnt7obdu28fHHH2OtZfr06QwaNIibb765xnFTpkzxhNvXXnvN0/Nbm8cee4zMzEwyMjJ48803mTBhQo1wDO7nvOoCwrrCMbgDd0ZGBhkZGcyYMYN77rmHG2+8kbi4OIqKiigtLT3mcaNHj+aDDz6gqKiI/Px8PvroI0aPHs2uXbuIiIjgd7/7HX/6059YvXo1AwYMYNeuXaxYsQJwf1jYsGFDnXWBuz8+OzubwsJCPvroo2P2gJ966qksWbKEQ4cOUV5ezrvvvsuYMWM47bTTWLJkCdu3bweo0WIxbNgwnn32WSZPnuz17DIdOnRg9+7dZGVlUVxczMKFC+s9Zvz48Tz//POeXuPqNfz6668kJiZ6dW5vKSA3kAlyf6qxtbzJRUSkpqKiIs80b+PGjWPChAk1Lqir6kGu+qq6EKk+VVNGDR06lMsvv7zOi77++te/cuqpp3L66acfdfFUfeLi4nj11Ve57LLLSE5OZtSoUfz8888AXHfddZx77rmkpqbWuV91FRUVXHnllSQlJTF48GBuvvlmoqOjvaplzJgxdO7cmf79+/OnP/2pRu9ubZ555hmWL19OcnIyAwYM4MUXXwTg/vvvZ/HixQwcOJAFCxbQqVOneu/rpptuoqioiKSkJK644gpef/31ekemjTF89NFHLFq0iF69epGYmMhdd91Fhw4dgH/3IFd9VYX52bNns3PnTs8FaldffTXx8fF89dVXvPPOO3z++eeeYz799FMA7rzzThYuXEifPn1YtmyZpx2kqv931qxZ3HfffSQkJDT69Hz1GTduXI2L5qobMWIEl112GcOHD2fkyJHceOONJCUlsW7dOoYPH86gQYN4+OGHufPOOwkODub999/ntttuIzk5mcGDB/PDDz/Ue/7hw4dzwQUXkJKSwmWXXXbM905CQgJ//etfGTt2LIMGDWLkyJFMmjSJ9u3b89xzz3mOr5pppEpV7/ukSZOO6k8+lpCQEO68807GjBnDhAkTGDBgQL3HXH/99XTo0IHk5GRSUlKYM2cO4J5CMCoqytN73ljM8V5F3BwMGzbMrlq1qknPWfDPp9hx7wt0m3kvYed6f+WxtAxLly5l7Nix/i5DfOBkfW3T09Pp37+/v8vwqby8PK/6Z6Vlai2v78qVK3n22WeZPXu2v0tpNhrjtX388ceJj4/3zF5S3bH+/TPG/GitHVbf/WoEuYFMYOUIcpku0hMRERHvDB8+nDPOOMMvC4W0ZjExMVx55ZWNfr+6SK+hghSQRUREpOGuueYaf5fgczfccINnru0qt912m89Wgrz66qt9cr8KyA1UNYKMpnkTERERqeH555/3dwmNQi0WDRUUAoAt00V6IiIiIq2RAnIDGU9A1giyiIiISGukgNxAJtAdkFFAFhEREWmVWmRANsZMNsa8kJOT0/Qn91ykV9b05xYRERERn2uRAdlaO99ae11UVFSTn9sEu5cyVIuFiIh3nE6nZ6GQlJQU/va3v3mmulq6dClRUVE1FopYvHjxUffRvXt3kpKSSE5OZsKECV6v2OVrM2fObPQFJz799FPPcxEREUG/fv0YNGiQz2YBqJKQkEB2dvZR299991369+/PuHHjjvu+d+/ezSWXXELv3r0ZOnQokyZNYvPmzWzevJnQ0NAar3/VSn25ubn8/ve/Jzk5maFDh5KamsrKlSvZvn07Y8eOZcCAASQmJvL3v//dc55Dhw5x9tln06dPH8455xyqBtI2bNjAqFGjCA4OZubMmUfVV15eTnJysmdJ6Yao/vwc+Xj+8Ic/ePZzuVykpqZ6vWpidXfdddcx677yyiv58MMPG3x/tVm6dCmJiYkMGjSo1lX/jsfZZ5+NXwY1T4BmsWggE1i51rdGkEVEvBIaGupZ3nj//v1cfvnl5Obmcv/99wPuZXYXLFhQ7/0sWbKE2NhY7rzzTh5++GFmzZrl1fkrKipwOp3H/wDqMHPmTK688krCwsK8Pqa+es455xzOOeccAMaOHcsTTzzBsGH1rmvgMy+99BKzZ89m5MiRXu1fXl5OQMC/44W1lqlTp3Ldddd5Vj9bs2YN+/bto3379vTr18/z/qju6quvpn///qxbt442bdqwZcsWfvnlFzp37szMmTMZNGgQubm5DB48mAkTJtC3b18eeughJk6cyH//93/z4IMP8thjj/HQQw8RGxvL008/XesqjU8++SQDBw48rg871Z+fzZs31/p45s+fz7Bhw4iIiGjwOZrKm2++yd13382llzbuQmiXX345zz//PH/+858b9X59qUWOIPtV1UV65ZrFQkRalq/n/MIHf1vdqF9fz/mlQTXEx8fzwgsv8Pe//53jXcn1zDPPZPPmzQDceOONjBkzhsTExBrLV3fv3p0///nPDBkyhPfee48XX3yR4cOHk5KSwkUXXeQJQtOnT+fGG29k5MiR9OzZk6VLl3qC2fTp0z3399lnnzFq1CiGDBnCb37zG/Lz85k1axa7d+8mNTWV1NTUWvc7Vj2zZs1iwIABJCcnNyiMvPTSS9x6662e2+eeey7ffPMN5eXlREdHc8cdd5CSksKoUaPYv38/APv27ePCCy9k2LBhjBgxwjNH7YEDBxg/fjyJiYlcf/31x3w97rnnHr7//numTZvGHXfcQVFREdOmTSMpKYkhQ4awbNkyT11Tp04lNTXVE+6rfP7550RERHDttdd6tg0ePLjOpcE3bdrE2rVruf/++zHGANCrVy8mTpxIp06dPMskt2nThlNOOYVdu3YB8NFHH3lWVJs2bZpndLV9+/YMGzasRnCvsn37dj7//HOuuuqqup76Yzry+anLW2+9xQUXXOC5PXnyZIYOHUpiYiIvvfSSZ/vChQsZMmQIKSkpTJgw4aj7ee6555g0aRLFxcU1tq9cuZIxY8YwdOhQJk6cyL59+9i0aRPDhw/37JOens6IESOOWd/zzz/P3Llz+ctf/sJ//ud/snjx4hoj6jfccANvvvkm4P5rw3333cfgwYNJTk7ml1/c/w7k5eUxbdo0kpOTSU5O9jz/F1xwAW+//Xadz09zo4DcQCbEPUpgyzWCLCJyPHr27ElFRYUnwH399dc1/sS+ZcuWOo9fsGABSUlJADz00EN89dVX/PTTT57vVWJiYli9ejWXXnopF154IStXrmTdunX079+fl19+2bNfVlYW3333HU899RRTpkzhj3/8Ixs2bGD9+vWsXbuWgwcP8uCDD7J48WJWr17NsGHDePLJJ7n55pvp1KkTS5YsYcmSJbXud6x6HnnkEdasWcNPP/3UaPPG5uTkMGbMGNatW8eoUaN45ZVXALj55pv5n//5H1atWsWcOXM8QfXee+8lNTWVDRs2cN5557F79+6j7vOBBx5g0KBB/POf/+SRRx5h1qxZBAcHs379et544w1+97vfef4Uv2bNGubOncsXX3xR4z7S0tIYOnRorXVv2rSpxuv/7bffsmHDBgYPHozDUXdM2bp1K2lpaZ4QeOjQIeLi4gDo3Lkze/bsqfd5u/XWW3n88cc9Qbwhjnx+ADZv3szgwYMZO3Ys3377rWffb7/9liFDhnhuv/baa/z444+sXLmSJ598kqysLPbu3cuNN97IBx98wLp163j33XdrnG/mzJl89tlnfPDBB4SEhHi2l5SUcMstt/Cvf/2LH3/8kSuvvJK7776bfv36ERoaSlpaGgCzZ8+u9YPADTfcwHnnncdTTz3F66+/Xu9jb9++PWvWrOHaa6/1vM/vu+8+4uLi+Omnn1i3bh1jxowBIDY2lry8vGO28DRXarFooH/PYqGALCIty+hL+vq7hGPytsUiNTUVp9NJcnIyDz74IABz5szh+eefx+VysWfPHjZu3EhycjIAv/3tbz3HpqWlcdddd5GdnU1+fn6NUc7JkydjjCEpKYn27dt7wndiYiIZGRlkZmayceNGz4hnaWkpo0aNOqq+77//vs79qteTnJzMFVdcwdSpU4+r7/VYQkNDmThxIgBDhw7l66+/BmDx4sVs2rTJs19WVhZFRUUsW7aMRYsWAe4RvsjIyHrP8c0333D77bcD7uenU6dOntH8CRMm0LZt2wbXfayWhLlz59Z7XG5uLhdddBFPP/30cbctfPjhh3Tp0qXW3veGSkhIYMeOHbRr144VK1Zw0UUXkZ6eTkREBLm5uTVacZ566inmzZsHQGZmJlu2bGHnzp2kpqbSrVs3ANq1a+fZf/bs2XTr1o25c+ceNRKenp7Ohg0bPH3iFRUVJCQkAO7V+2bPns2jjz7Ke++9x5o1a074cQJceOGFgPu9VvU+Wrx4sWfU2BhT4/0QFxfHnj17iI6ObpTz+5oCckMFBgEaQRYROV5bt27F6XQSHx9Penq618dV9SBX2bZtG0888QRffvklXbt2Zfr06TX+7BweHu75efr06Xz44YekpKTw6quvsnTpUs/vgoPdsxM5HA7Pz1W3y8vLcTqdjB8/nnfeeafO+qy1de5XvZ6FCxeybNky5s+fz0MPPcT69euP+ef/IwUEBHgucARqPN6goCDPz06nk/Lyck9dK1asqPF7X6j++KpLTEz06gPQkcesXbu2xmOtrrS0lAsvvJCrrrqKKVOmeLbHxMRw4MAB4uLi2LVrFx07dqzzPN9++y1z585l3rx5FBcXk5uby7Rp03jttdc8+2RkZHg+xMyYMaNGq8iRQkJCPCO7I0aMoFu3bmzevJlBgwbVGA1fvHgxy5Yt4/vvvyc0NJQzzjjjqJaJIyUlJbFmzRp27drlCdBVrLUkJyd7PhRV95vf/IaHH36Y008/nVGjRnkdUOt6r8G//7up/l6rS3FxMaGhoV6duzlQi0UDmQD3hRUKyCIiDXfgwAFuuOEGZsyYcVx/0q4uNzeX8PBwoqKi2LdvHx9//HGt++bl5dGxY0fKyso8syR4a+TIkSxfvtwzUlpQUODpuYyMjCQvL6/e/apzuVyekcJHH32UnJwcr2c26N69O2vWrMFaS0ZGBj/++GO9x4wbN45nnnnGc7tqtPbMM8/09IXOnz/f8zjqMnr0aM/zl56ezp49e+jdu3edx0yYMIHc3FxPywfAunXrWL58ea3H9OvXj6SkJB544AFPb/S2bdv4+OOPsdYyffp0Bg0axM0331zjuClTpnjC7WuvvVaj5/dYHnvsMTIzM8nIyODNN99kwoQJNcIxuJ/ztWvXsnbt2jrDMbjf3xUVFYC71WLr1q306NEDgN69e5ORkQG422HatWtHaGgoGzZsYOXKlQCcdtppLFmyhO3btwNw+PBhz30PGzaMZ599lsmTJx81i8uAAQPYtWsXK1asANwfIDZs2ABAWFgYZ511FjNmzGhQn3W3bt3YsGEDpaWlZGVl8eWXX9Z7zPjx4z3vNWstWVlZgHtE++DBg3Tt2tXr8/ubAnIDmaorj734tCQiIlBUVOSZ5m3cuHFMmDChxgV1R/Yg1zbTwJFSUlIYPHgwQ4cO5fLLL6/zoq+//vWvnHrqqZx++umccsopDao/Li6OV199lcsuu4zk5GRGjRrFzz//DMB1113HueeeS2pqap37VVdRUcGVV15JUlISgwcP5uabb/Z6VG/MmDF07tyZ/v3786c//clzsVpdnnnmGZYvX05ycjIDBgzgxRdfBOD+++9n8eLFDBw4kAULFtCpU6d67+umm26iqKiIpKQkrrjiCl5//fV6R6aNMXz00UcsWrSIXr16kZiYyF133UWHDh2Ao3uQqwLW7Nmz2blzJykpKQwcOJCrr76a+Ph4vvrqK9555x0+//xzzzGffvopAHfeeScLFy6kT58+LFu2zNMOkpmZSUJCArNmzeK+++4jISGh0afnA/dfOZKTkxk0aBC//e1vefHFF6maknbSpEmev1xMmjSJwsJCBgwYwF133cWpp54KuPt6n3vuOS644AJSUlK44ooratz/mDFjeOSRR5g0aVKN8BwcHMz777/PbbfdRnJyMoMHD+aHH37w/P6KK64gMDCQs88+2+vH0qNHD6ZOnUpiYiKXXnppjf7p2tx7773s27ePgQMHMmjQIM+I9sqVKznjjDPq7SlvTszxXkXcHAwbNsyuWrWqSc9pXS5+HpBI7PiexD29sEnPLb63dOlSxo4d6+8yxAdO1tc2PT2d/v37+7sMn8rLy/Oqf1Zaptby+mZmZnLttdfyySefNPm5H3nkEUpKSmp8MG1Kf/jDH7jkkks8F+1V8fVre6x//4wxP1pr6503UT3IDWQcDjAWqxFkERER8VJCQgLTp08nPz+/SedCnjx5Mjt37vSqRcJXBg8efFQ4bu4UkI+Hw2DLFJBFRETEe429AIc35s+ff9S2KVOmsGPHjhrbnnjiiRNaLbEu9fVuN0cKyMfBOMFqmjcRaSGstSd8QZyItB5V08u1ZifaQtxyuqWbEXdA1giyiDR/ISEhHDp06IT/ZyEi0lJYazl06FCNxVQaSiPIx8E4DLa8wt9liIjUKyEhgczMTA4cOODvUnymuLj4hP5HKM2bXt/Wy5evbUhIiGexlOOhgHw8Agy2TAFZRJq/wMBAzzysrdXSpUsZPHiwv8sQH9Hr23o159dWLRbHw+lQi4WIiIhIK6WAfByM0+AqP/bylyIiIiLSsikgH48Ah3qQRURERFopBeTjEeDElmkEWURERKQ1UkA+DjYgQCPIIiIiIq1UiwzIxpjJxpgXcnJy/FNAQAC2QnOKioiIiLRGLTIgW2vnW2uvi4qK8s/5AwKwFUCFVtMTERERaW1aZED2NxsYgKsCKC3wdykiIiIi0sgUkI+DDQjCugyUFfq7FBERERFpZArIx8EGBmIrDJQqIIuIiIi0NvUuNW2McQApQCegCEiz1u73dWHNmQ2sGkFWi4WIiIhIa1NrQDbG9AL+DIwDfgUOACFAX2NMIfAP4DVr7Uk3IbANDMZWgC0txPi7GBERERFpVHWNID8IPAdcb62tMaeZMSYeuBz4HfCa78prnlxBwTgxUJjr71JEREREpJHVGpCttZfV8bv9wEyfVNQCuAKC3N+LcnH6uRYRERERaVz1XqRnjAkzxtxtjHmx8nYfY8z5vi+t+XIFhQBgC/L8XImIiIiINDZvZrGYDZQAoypv78LdfnHS8gTkQgVkERERkdbGm4Dcy1r7GFAGYK0thJP72jRXUCgAtijfz5WIiIiISGPzJiCXGmNCAQue2S1KfFpVM+cKrArImuZNREREpLWpdx5k4F7gE6CLMeYt4HRgui+Lau5sUDAALo0gi4iIiLQ69QZka+3nxpjVwEjcrRW3WGsP+ryyZswGup82W6yV9ERERERam7oWChlyxKY9ld+7GmO6WmtX+66s5s0GVAbkIgVkERERkdamrhHkv9XxOwuc1ci1tByBgQDYkiI/FyIiIiIija2uhUJSm7KQlsQzglxa6udKRERERKSxeXORHsaYgcAAIKRqm7X2dV8V1dx5AnJZuZ8rEREREZHGVm9ANsbcC4zFHZAXAROBb4CTNiBXtVi4Ssv8XIiIiIiINDZv5kG+GDgb2GutvQpIAaJ8WlUzpxFkERERkdbLm4BcZK11AeXGmDbAfqCLb8tq3jwBuVwBWURERKS18aYHeZUxJhp4EfgRyAe+82lVzV3VLBalCsgiIiIirY03C4X8V+WPzxtjPgHaWGt/8m1Zzdu/R5Ar/FyJiIiIiDS2elssjDH/YYyJArDWZgA7jDFTfV1Ys+Z0AgrI06ivjAAAIABJREFUIiIiIq2RNz3I91prc6puWGuzgXt9V1ILYAwmwOAqU0AWERERaW28CcjH2ser+ZN9xRgz2RjzQk5OTv07+6qGAAe2zOW384uIiIiIb3gTkFcZY540xvSq/HoK98V6fmOtnW+tvS4qyn+zzZkAB7ZCAVlERESktfEmIN8ElAL/rPwqBv7gy6JaAhPgxJYrIIuIiIi0Nt7MYlEA3AFgjGkLZFtrra8La+4cgQrIIiIiIq1RrSPIxph7jDGnVP4cbIz5EtgM7DPGjGuqApsr9wjySf85QURERKTVqavF4rfApsqfp1XuGw+MAR72cV3Nnglw4qpQQBYRERFpbeoKyKXVWinOAd6x1lZYa9Px8ywWzYEJCnCPIKvbRERERKRVqSsglxhjBhpj4oBU4LNqvwvzbVnNnwkIwLoAl+ZCFhEREWlN6hoJvgV4H4gDnrLWbgMwxpwHrGmC2po1ExiAy2XAVQbOk35AXURERKTVqDXZWWt/AE45xvZFwCJfFtUSOIICKK8wUFEGgaH+LkdEREREGok38yB7GGMW+KqQlsYEBmJdlQFZRERERFqNBgVkoLNPqmiBTGCgu/3YpYAsIiIi0po0NCCf9L3HVUxwELZCI8giIiIirU2DArK19mpfFdLSmKCqFotSf5ciIiIiIo2o3ukXjDHrgSMn+80BVgEPWmsP+aKw5s4RFOweQXaV+7sUEREREWlE3sxP9jFQAbxdeftS3PMg7wVeBSb7pLJmzoSGYF0GW1KE8XcxIiIiItJovAnI46y1Q6rdXm+MWW2tHWKMudJXhTV3jpAQAFxFBTj9XIuIiIiINB5vepCdxpgRVTeMMcPBkwlP2v4CR6h77mNbVOjnSkRERESkMXkzgnwt8IoxJgIwQC5wjTEmHPhfXxbXnJnKgOwqyPdzJSIiIiLSmOoNyNbalUCSMSaq8nZOtV/P8VVhzV3VCLKrUCPIIiIiIq1JvS0WxpgoY8yTwBfAF8aYv1WF5ZOZCQ0DwBYX+bkSEREREWlM3vQgvwLkAZdUfuUCs31ZVEvgCAsHwFVY4OdKRERERKQxedOD3Mtae1G12/cbY9b6qqCWwhFWOYJcpBFkERERkdbEmxHkImPMGVU3jDGnAyd9KjShlSPIxcV+rkREREREGpM3I8g3AK9X6zvOAqb5rqSWwdNioRFkERERkVbFm1ks1gEpxpg2lbdzjTEXAT/5urjmzBEeCYDVCLKIiIhIq+JNiwXgDsbW2tzKm0/5qJ4Ww1QGZFdxiZ8rEREREZHG5HVAPoJp1CpaIEflNG8KyCIiIiKty/EGZNuoVbRAJjgU47BYBWQRERGRVqXWHmRjzHqOHYQN0N5nFbUUziBMgMVVooAsIiIi0prUdZHe+U1WRUvkCMThtLiKS/1diYiIiIg0oroC8g5rbZ2tFMYYU98+rZbDgSMArEaQRURERFqVunqQlxhjbjLGdK2+0RgTZIw5yxjzGifhfMhzHl7J/jT3ZwITaHSRnoiIiEgrU9cI8rnA1cA7xpgeQDYQAjiBz4CZ1to1vi+xeSnKKyXA6Q7IjkAHtqTMzxWJiIiISGOqNSBba4uBZ4FnjTGBQCxQZK3NbqrimqPg8EBKK9sqHEFOXArIIiIiIq2KV9O8WWvLrLV7TvZwDBAaEUh5ZVeFCXTiKi33b0EiIiIi0qiOdx7kk1ZIRCAVlRNXOIIDsWUV/i1IRERERBqVAnIDhYQHUlE1ghwciKtUAVlERESkNak3IBtjwo0xjsqf+xpjplT2JPuNMWayMeaFnJycJj2vtZZSB1SUgstlcQQH4Sp1NWkNIiIiIuJb3owgLwNCjDGdcc9e8TvgVV8WVR9r7Xxr7XVRUVFNel5jDK+s2gFASWEZjrAQXGXu4CwiIiIirYM3AdlYawuBC4FnrbW/ARJ9W1bzFRDpHrUuzi/DERoCFmxxsZ+rEhEREZHG4lVANsaMAq4AFlZuc/qupObtUJsPgGoBGXAVFPizJBERERFpRN4E5FuBvwAfWGs3GGN6Akt8W1bzZYPcT1lRfhnOMAVkERERkdamrpX0ALDWfgV8BVB5sd5Ba+3Nvi6suQoIdV+fWJhXSlzVCHJ+vj9LEhEREZFG5M0sFm8bY9oYY8KBNGCjMeZ235fWPIWGhwJw6HARjrAwAFz5uf4sSUREREQakTctFgOstbnAVOBjoAfumSxOSrGRbSk3ZRzOKsZRGZYr8hSQRURERFoLbwJyYOW8x1OBedbaMuCkndesY2Q8xYEF5GQX4QiPAMCV17TzMYuIiIiI73gTkP8BZADhwDJjTDfgpB0y7RYVT3FAAXm5hTjCK1ssNIIsIiIi0mrUG5CttbOstZ2ttedZt+1AahPU1iwlVAbkksJSHBGVI8jqQRYRERFpNby5SK+9MeZlY8zHlbcHANN8XlkzFRcaR3FgPrbEhSM8EtAsFiIiIiKtiTctFq8CnwKdKm//gntu5JNSTGgMJQGFOEvBBIVgnC5cBQrIIiIiIq2FNwE51lo7B3ABWGvLgQqfVtWMxYTGUBSQT2BFAC4ThCPQ4srXQiEiIiIirYU3AbnAGBND5cwVxpiRwEk7bUOwM5jSgBIcGIrLgnAEWFyFCsgiIiIirUW9K+kBtwHzgF7GmOVAHHCxT6tq5lyB7lnuDuQZHIEuXAWFfq5IRERERBqLN0tNrzbGjAH6AQbYVDkX8knLVAbk/TkQH2BxFRb5uSIRERERaSzejCADjAC6V+4/xBiDtfZ1n1XVzAWFuDtTDuZYOgRayhWQRURERFqNegOyMeYNoBewln9fnGeBkzYgh4UGAnA434EjwIWrqNjPFYmIiIhIY/FmBHkYMMBae9IuL32kNmHBABzKB0eApSJHAVlERESktfBmFos0oIOvC2lJIoPCqDDlZBWUuad5Ky7xd0kiIiIi0ki8GUGOBTYaY1YAniRorZ3is6qauTYBbSgKyKe4KAhHoMGWlGNdLozDm88bIiIiItKceROQ7/N1ES1NpDOSw4EF2BIHjlB3u4WrsBBnRISfKxMRERGRE+XNkOd51tqvqn8B5/m6sOasjbMNJQEFOMstjrAQAFwFWixEREREpDXwJiCPP8a2iY1dSEsS6YykOLCAkAonhIYCCsgiIiIirUWtLRbGmBuB/wJ6GmN+qvarSGC5rwtrzgJNIGWBJYS6AigPDwPyFJBFREREWom6epDfBj4G/he4o9r2PGvtYZ9W1QKYEEtwRRAlQWGARpBFREREWou6WiystTYD+AOQV+0LY0w735fWvAWGOXDgIDegLaCALCIiItJa1DeCfD7wI+6V80y131mgpw/ravbCIoMAOGyiiQFcWm5aREREpFWoNSBba8+v/N6j6cppOSIi3bNXHK6IIgawxYX+LUhEREREGkWtLRbGmBnVfk5smnJajqiocAD2lrm/u/Jz/FmOiIiIiDSSunqQr6728xu+LqSlaRvdBoB9ZYEAWAVkERERkVbB27WRTf27nFxioqMByLVVC4Xk+rMcEREREWkkdV2kF22M+Q/cIbqNMebC6r+01s71aWXNXHxULGmOXVgbgnFYbGG+v0sSERERkUZQV0D+CphS+fMyYHK131ngpA7IsWGx5AenEeAKwTgtLgVkERERkVahrlksrmrKQlqamNAY8oKyCCuOxzgttlDzIIuIiIi0Bt72IMsRgp3BlIblE14RhsNpcWmaNxEREZFWQQH5BJiIckLLQ3EFBGCLi/1djoiIiIg0AgXkExAY5Z7cozikLS4FZBEREZFWoa6L9DyMMacB3avvb6193Uc1tRjhbd3LTReGxBJZfMDP1YiIiIhIY6g3IBtj3gB6AWuBisrNFjjpA3J0rHsVvYLgWCryd/m5GhERERFpDN6MIA8DBlhrra+LaWliY6M5jIv84Bhch8r8XY6IiIiINAJvepDTgA6+LqQlah8ZT0FQLgXB7XCVVNR/gIiIiIg0e96MIMcCG40xK4CSqo3W2im1H3JyiA+NJz94G8VBMdgSl7/LEREREZFG4E1Avs/XRbRUcWFx5AVlURrQHcrAlpdjAry67lFEREREmql605y19qumKKQligmNIT84iwpHChZDeW4uge3a+bssERERETkB9fYgG2NGGmNWGmPyjTGlxpgKY0xuUxTX3AU6ArERpUAApUGRHMzc7e+SREREROQEeXOR3t+By4BfgVDgWuAZXxbVkgS2cX8vDm7H3m0Zfq1FRERERE6cVyvpWWs3A05rbYW1djZwrm/LajnC27kXCykOaUdWZqafqxERERGRE+XNFWWFxpggYK0x5jFgD1qi2iM6xr1YSElIW1xbNvu5GhERERE5Ud4E3d9V7jcDKAC6ABf5sqiWJDa6HSXOQkrConFuz/B3OSIiIiJygryZxWK7MSYU6Gitvb8JampR4kPj2RCcTXFEW6K37/N3OSIiIiJygryZxWIysBb4pPL2IGPMPF8X1lLEh8WTF3yYouAYwgryySnSktMiIiIiLZk3LRb3ASOAbABr7Vqghw9ralHiw+LJD8qiKKAdYUXF/Jx52N8liYiIiMgJ8CYgl1lrc47YZn1RTEsUFxZXuVhIBBXOYLb8vMPfJYmIiIjICfAmIG8wxlwOOI0xfYwxTwPf+riuFiM6OJrCEPfnh5LgtuzarIAsIiIi0pJ5E5BvAhKBEuAdIBe41ZdFtSQO4yCwjQEq50LerrmQRURERFoyb2axKAT+X+WXHENYW/fTWBQSQ/GevZRXuAhwaqpoERERkZao1oBc30wV1topjV9OyxTdLoJyRzGF4e1pW3CAbQcL6NM+0t9liYiIiMhxqGsEeRSwE3dbxQ+AaZKKWqD48HhyQvdTFNmB2H2b2bgnVwFZREREpIWqqw+gA3AnMBD4P2A8cNBa+5W19qumKK6liAuN42DYXgpCO9C54CDpe/L8XZKIiIiIHKdaA7K1tsJa+4m1dhowEtgMLDXGzGiy6lqI+LB4skL3URzYloTCbH7ele3vkkRERETkONV5kZ4xJhiYBFwGdAdmAR/4ohBjTH/gFiAW+MJa+5wvzuMLcWFxZIXuBaA0OJbDv2zB/ZlCRERERFqaWkeQjTGvA98BQ4D7rbXDrbV/tdbu8vbOjTGvGGP2G2PSjth+rjFmkzFmszHmDgBrbbq19gbgEuD043o0fhIfFk926D4ACsM7YA8f4mB+iZ+rEhEREZHjUVcP8pVAH9yjut8aY3Irv/KMMble3v+rwLnVNxhjnMAzwERgAHCZMWZA5e+mAAuBRQ16FH7WMbwjeSGHgTIKwjoQWVpI+h5vnyIRERERaU5qbbGw1p7wRL7W2mXGmO5HbB4BbLbWbgUwxrwLXABstNbOA+YZYxYCbx/rPo0x1wHXAbRv356lS5eeaJkNlp+ff9R54wKiKAvcR0FYByLKDrPgm7VU7Aps8trkxBzrtZXWQa9t66XXtnXT69t6NefXtt6FQnygM+7p46pkAqcaY8YCFwLB1DGCbK19AXgBYNiwYXbs2LE+K7Q2S5cu5cjzzv9yEIfC9hEZ3pWO5fvICY1l7NjBTV6bnJhjvbbSOui1bb302rZuen1br+b82vojIB+TtXYpsNTPZRy3U2ITWR75Cx2yB9Ot1MW7arEQERERaZH8sR7yLqBLtdsJldtatH7tTnFfqGccxDiD2bw/n6LSCn+XJSIiIiIN5I+AvBLoY4zpYYwJAi4F6lzWuiXo27YvWZUzWQQTgsvCz3s1iiwiIiLS0vg0IBtj3sE9VVw/Y0ymMeYaa205MAP4FEgH5lhrN/iyjqbQPqw9NvQQWBeu8iAA0nYrIIuIiIi0ND7tQbbWXlbL9kW0sKnc6mOMoW9QGM6KAxSWhxEdFsjG3Tn+LktEREREGsgfLRatVt+QOErZS74jioEd25C2SyPIIiIiIi2NAnIj6hfZlYLAfRSGxDEoysGmvXmUVbj8XZaIiIiINIACciPqF5dCdsherMNJ34oSSitc/Lov399liYiIiEgDtMiAbIyZbIx5ISenefX49koYxaGIvQC0yy8GIE19yCIiIiItSosMyNba+dba66KiovxdSg2B7XrjiNwDgM0qITzIyUbNZCEiIiLSorTIgNxsOQOIDy8juPgw2YfL6N+xDWm7NIIsIiIi0pIoIDeyPiGRBBfvJTvPMLBzFBv35OJyWX+XJSIiIiJeUkBuZH1D4qFiD/nlofTvGElhaQXbDhX4uywRERER8ZICciMbENmdUsc+XCaQ3mGhAGqzEBEREWlBFJAbWVR8Ita5G4DocghyOnShnoiIiEgLooDc2OL6ERGUCUDWjixO6RjJyozDfi5KRERERLylgNzY2vUgPjybwNJcdvycwcSBHVm9I5uMg+pDFhEREWkJFJAbW0QHekcWEl6wl6xdefzH4M44DPxrdaa/KxMRERERLyggN7bAEBKiwgks20tJUSjt2wQzuk8c//oxkwpN9yYiIiLS7LXIgNxcl5qu4mjTERu4H2tCKMwt5eKhCezOKea7LYf8XZqIiIiI1KNFBuTmutS0R0QcoZEHAdicvoXxA9rTJiSA93/c6efCRERERKQ+LTIgN3sh0SSE7wXgl582EBLoZMqgTnyyYS+5xWV+Lk5ERERE6qKA7Auh0fQJ2kdAeSGHdmQDcPHQLhSXuVj40x4/FyciIiIidVFA9oWQKEKDcggr2EtplhOAlIQoesdH8M+VarMQERERac4UkH0hJBpnQBFhxftxlLcjvzQfYwyXj+jK2p3Z/JSZ7e8KRURERKQWCsi+EBqNMRDmzMOYNny3bQUAFw9LICzIyWvfbvdzgSIiIiJSGwVkXwhtC0BMZAUAS9d9B0CbkEAuHNKZ+T/t5lB+id/KExEREZHaKSD7QkR7AOKjCwHYszmH3NJcAKaN6k5puYt31YssIiIi0iwpIPtCZEcA2rQPJCJvJ10O9uTzjM8B6NM+ktN6xfDW99spr3D5s0oREREROQYFZF+I7ABAeK9I2mb/QlxhD+b/ssDz62mndWd3TjGL0/f5q0IRERERqYUCsi8EhUNIFEGh+cRHFGEIZM/WbHbl7wLg7FPi6RwdysvfbMNa6+diRURERKQ6BWRfadcLc3gLPc4ZjLEV9NnfhwVb3KPIAU4H14/pycqMLJZuOuDnQkVERESkuhYZkI0xk40xL+Tk5Pi7lNrF9oWDvxJ1+qlE5u3glOwkFmxd4BkxvmxEV7rHhPHIxz9T4dIosoiIiEhz0SIDsrV2vrX2uqioKH+XUru4vpC7i+BuHYgNzMJR1oHMw7tYe2AtAIFOB7efcwqb9uXxr9WZfi5WRERERKq0yIDcIsT2BcAc2ky3Ub2wxsnQfYm8lf6WZ5fzkjqQ0iWaJz/7heKyCn9VKiIiIiLVKCD7Smw/9/eDv9DtrGSMq5zTsk9l8fbF7C3YC4AxhjsnnsLe3GJeWb7Nj8WKiIiISBUFZF9p2839PWs7YX16El26h8CsOCyWOZvmeHY7tWcMZ58Sz3NLt5BVUOqnYkVERESkigKyrwQEu1fUy83EGEOHOBe5jhjOihvPe7+8R3F5sWfXP088hYKScv6+ZLMfCxYRERERUED2rTadIcd9AV7XLk4wDs7cP4nskmw+3vaxZ7e+7SP5zdAuvPHddnYeLvRXtSIiIiKCArJvRXeBrAwA4nvHEr9/NQdWQf/QJN5Mf7PGIiF/HN8XhwOe+GyTn4oVEREREVBA9q34AXB4G5QWENCxIz22zae83DIx60p+yfqFH/b+4Nm1Q1QI15zRg4/W7iZtVzOe31lERESklVNA9qX2iYCF/ekE9+lDBHl0DdpF8fpQutKLl9e/XGP368f0om1YII9+8rN/6hURERERBWSfaj/Q/X1fGs6ICKIumELC8hcxwOTsq/h+z/dsOLjBs3ubkED+a2xvvv71IKt3ZPmnZhEREZGTnAKyL0V3g6BI2PMTAG0vv5zg/AP0ic/D9XMkHcq78nJazVHky0/tSnRYIM8u2eKPikVEREROegrIvuRwQM8xkPY+lBUR0rcvwX370vHHd8DAlNLfsXj7YjJyMjyHhAcHMP207ixO38emvXn+q11ERETkJKWA7GvJv4XiHNifDkDcLTdjNq2lU0QuoZs7EkwIr254tcYh00/rTliQk+e/0iiyiIiISFNrkQHZGDPZGPNCTk4LmO0hfoD7e2VAjjz7bMLPOIOOv3xCSUEFvwm4mnlb5rG/cL/nkOiwIK44tSvz1u3WvMgiIiIiTaxFBmRr7Xxr7XVRUVH+LqV+7XqAMxgOpHs2hY0YQeTPy4iKCabLzmQqbAVvbnyzxmHXju6J0xiNIouIiIg0sRYZkFsUhxPi+npGkAHChgzGYOndqZDDGcWcH3UR/9z0T3JK/j0i3r5NCL8ZlsA7K3bwRfo+f1QuIiIiclJSQG4K8QNg12oocV90F5qSgiMykg47v8EZ6GD4oXMoLC9kzqY5NQ6787z+JHaKYsbba1i7M9sflYuIiIicdBSQm8LQ6VB0GH74BwAmMJDI8eMp/nQ+vZKi2beumDNjz+LN9DcpLi/2HBYeHMAr04cTFxnMNa+uJONggZ8egIiIiMjJQwG5KXQ7DbqPhnXvejbFXDUdW15O59VvU1FWwdh9F3O4+DAfbv6wxqFxkcG8etVwXNZy5cs/8Ms+Tf0mIiIi4ksKyE2l33lw6FfI3gFAcJ8+xM2YgVk6n27OHRxcVc6I0DN4Oe1lsoprrqLXMy6C164eQUm5i6nPLOeTtD3+eAQiIiIiJwUF5KbSbZT7e+Yqz6aY635P9G8uJmHZ8zgDDOP3XMHhosPcsuQWSipKahyenBDN/Bln0Ld9JDe8uZonPt2Ey2Wb8hGIiIiInBQUkJtKfKJ7uredKzybjDHE/P73BJXk0C9yF4fSS7mr+8Os2b+Ge5bfg7U1A3CHqBD+ef1ILhmWwN+XbOba11eRU1TW1I9EREREpFVTQG4qAUHQZzysnwPl/x4dDuralTZTJhPz0eOEhRnKv2nHTSk3s2jbIp5d9+xRdxMc4OTRi5L56wWJLPvlAFOfWc6v6ksWERERaTQKyE1p2NVQeAg2zquxucM99xLauQM9Vr/C/u15JG1LZWrvqTy/7nnmbZl31N0YY/jdqO68/fuR5BWXMfWZ5cxdndlUj0JERESkVVNAbko9U6FtD1j5Uo3NzohwurzwD7onWDrtXs7qT3dwVZubGNFhBPd+ey8r96485t2N6NGO+TedQWKnKG6bs45b311DXrFaLkREREROhAJyU3I4YPg1sPN798Ih1QR160bXl18mJWY74fm7+PKlNB4c9AhdIrtw65Jb2Zaz7Zh32TEqlHeuG8lt4/sy/6c9THhqGQ8vSmfFtsNU6CI+ERERkQZTQG5qg38HYbHw8f+Ay1XjV47gYLo/M4vB+Z9RXljCsidX83+jnybAEcCNi29kb8HeY96l02G4+ew+zLl+JL3jI5i9fBuX/OM7Rjy0mMc//Zm9OcXHPE5EREREjqaA3NRCo2H8/ZC5EtLeP+rXjrAwBjz7KCmu7zmY7WTNrC08feYz5JTkcO1n13Kg8ECtdz20WzveuOZUVt89nmcuH8KQbm15dukWznj0S2a8vZolP++nrMJV6/EiIiIiooDsHymXQeehsOCPcGDTUb8O7NSJU5/7fyRlf86efZDxajbPnPl39hfu59rPruVQ0aE67z4yJJBJyR158T+H8dV/pzLttO4s++UAV726kuEPLeYvc3/ii/R9FJVW+OoRioiIiLRYCsj+4HDCRS+5v795MZQWHr1LUBAj7p3GKRlz2bm9lC1PbOfJoU+zO383V396Nbvzd3t1qq4xYdx9/gBW3jWOl/5zGGP6xjFv7W6ueW0Vgx74jKtmr+CN7zLIzDq6BhEREZGTUYsMyMaYycaYF3JycvxdyvFr1xMufRtydsDSh4+5S8iAAYx5415SzGoOFoSx4ZlC7gmbycHCQ1yx6ArSD6V7fbrgACfjBrTn/y4dzOp7xvPGNSO4bERXthwo4O6PNnDGo0s4d+Yy3lmxg9JytWGIiIjIyatFBmRr7Xxr7XVRUVH+LuXEdD8Dhk6Hb5+GH1875i4BcXEMnTGRUesfp23OZnZ8VsLv0x+m+/5krvr4KpZlLmvwaYMDnIzuE8d9UxL56vaxLL5tDP/vvP4EOh38Ze56xj6+hNe/y9CUcSIiInJSCvB3ASe9856AQ1tg/i0Q2hYGTDlql9DERPr83/8SdM015AycwLaYCxiWdgEDws/k6d1vsHbMT/zX8BsIcDT85TTG0Ds+gt7xEVw7ugfLfj3I01/8yj0fbeDBhemM7RvH+SmdGNc/nrAgvV1ERESk9VPi8TdnoLvV4rXJ8N40GHMHnPnf7v7kasJPHUGXWf9HwL33Ev3zEsr/8BBpezsTtvUySjOKefDLl7n4grNJ7N8LY8xxlWKMYUzfOM7sE8uandnMW7ubRev38NnGfYQEOjj7lPacn9yR03rFEhUW2BiPXkRERKTZUUBuDkLawPSFsPA2dz/y5sVw/lPQYWCN3SLPOovQ5GQyZ9xE0d9uY0hMDK7zp/FDUEfMlm58NWsHn0Wnccrp7Rlz5hDCo4KPqxxjDEO6tmVI17buC/wyDrPgp918vH4vC9fvAaBXXDiDu7ZlZM8YRveJpX2bkBN+GkRERESaAwXk5iI4Av7jH+7lqD+/G145B0b/CUbeCIGhnt0CYmPp9sbr5CxcSN7ixeS/9iSnBgTA6DNZ3r0Phw/GsG1hG7YtXI6NK6RnUjxDR5xCfNdIjKPhI8tOh2FkzxhG9ozhvsmJrNqexY/bs1izI4svf97P+z9mAtCvfSRn9Y/n3MQOJCdEHfcotoiIiIi/KSA3J8bAoMvcF+8tuh2+uB9Wvgxn3w1Jl7iXqgZMYCDRU6cSPXUqJVu3kv3e++R8+CGnL/kSZ6eObB01gp8DOSMtAAAVjklEQVQCoijMbw9fhrDty1XY0DI69o9k0PDedOnfjqCQhr/0AU6HJywDuFyW9L25fPPrQb765QAvLtvKc0u30DEqhFE9Y0hKiCI5IZqBndsQHOCs595FREREmgcF5OYougtc/i5s+xo+uws+uB6+/bs7PJ8yCdp29+wa3LMn7f/8P8T98Vbyv/iC7Pfeo9u/PqIbgNNJbq+erBjQn102ntJ1/di7Og3rcBHdPYgBQ7rSMzme6Piw4yrT4TAkdooisVMU14/pRXZhKV+k7+ezjXtZ9utB5q7ZBUBIoIPh3dsxqlcMp/WKJalzFM7jGM0WERERaQoKyM1Zj9Hw+yXuJam/mQmf3un+ap8EvVLdX93PBGcAjqAg2kycSJuJE6nIzqZo3ToK164l6PPPGffhPAAK+3Rl5YBebAyKIW5Pf3LeL+e797diosqI7RpBjx6daN+9DR17RxMY1PAR3+iwIC4amsBFQxOw1rIvt4S1O7P5YdshvttyiMc+2QRsIjIkgFN7xJDUOYou7ULp0i6MLm3DiI8MxqHgLCIiIn6mgNzcORyQfIn76/BW+HkRbFoE3z8H386C4DZwyvnQeQh0SILOQ3FGRxMxZgwRY8YQN2MGxRs3UrhiJdkfzGXMR0sY43RS3HcVGR1i+DWkAwWlncndlMCB9SWV57S06xFCn4Gd6NAjivhubQgKbdhbxRhDh6gQzo36/+3de3BcZ3nH8e+zZ7W7WkmWLFu+e+LLpEAu5ELIJFxdoEApSYBmIFM6QKFDS8stDM2E0hmgnbZcwrUwpSFQoA0Q6qQkDS1JgJgAIfernTiJZSd2HMeWLFvWdXe15+kf55V0JEuy5FiWtfl9Zt4557znfc95z757pOe8e87uMt542jIAOnpK3LF9P7e37+d37Z38/NG9Y+rkshlWLaxnZUuSljfXs7q1nnVtjaxra2BBQd+cISIiIrNPAfJ80roOXvbBJJX7k0B52y/gsZ/Cgz9IyuQaYdVLk7TsNGzpadSfeir1p5/Oove9l1J7O93X30D/ffdSvGsrL+y7FwCPMnStWMzTzcvpaFrPnu0voqs9BMw4xbYsK9a2smLdQhavbmLhsiKFhpkFrG1NeS44YwUXnLECgMFKld0HB9jV1c+uAwM83dXPzq5+njk4wKN7eujsLY2pv7gxz7q2BtYtbgjTRta2NbB6YZFcdl7+5o2IiIicgBQgz1e5Ipx+cZLc4dAzsPse2HEb7LwDfn0FePjJ6LoiLDkFlp5KftnpLLngNPiTP8Abl1HZ38fg1scY3LKFpvZ2Vu7bR2nzzXjPdVSyDextW8PeRavpHVzD4/tOYttdC0aakCnGNC7NsXRlC8tWttKypJ6mRQWaWgtkp3GLRqEuYn1bI+vbGidcXxqqsqtrgO0dvWzv7GNHRx/bO3u55ZG97O8rjynb2pBjSVOetlRa0lQYyRueNuaz+oYNERERmZIC5FpgBs0rk3TKRUleuR86tsLeLbB3czJ95Hq4b/QnrQ3Ita4jt+pcFpy6GM5dDwtejTevptwV03vfo7Q9+RQn7drOwDMPEO3eRyXbTG/jSvqKy+huWsah/ct4on0ZT2Q6xzQp21yleVWBFWsWsWxZK40tBYrNORqa89Tlp3d/cz4bjfzK33jd/RXaO3vZ0dHHrgP9dPSU2NdToqOnxPaOPjp6SpSr8WH1CnWZJIBuHA2iVy2sZ9XCIitaCuzrj+kZrCiQFhEReR5TgFyrcsXkvuSVZ4/mDY80dz8NPc/AwV3w+E3w1O3Q3wmVfiAJnPMhUVzA8jNb4PxWqpn1DFUbGCrXM7i/hwMde+jbc4B470Hoq2OwsJiBwiIGC4vobVjBwX2r2b+lwsM8O7ZtVsZyg2SbyhQWQ+PyIk1LFtLSvJCFTQtobWqhUMyRK0REdZkJA9XmYt3Ij5lMxN3pHqiMBM37egbp7Cmzr2eQjp4SHb0ldnT28bv2/RwaHBpT97LbbibKGMW6iEIuoiEX0dqQY3FjnkWNORrzWYq5LMVcRDGfpSEXUcxlachHSV4uS0MuSzEs57ORvrVDRERkHlGA/HySHmke9vIPj86X+5Lg+eDOJPV1wsCBkLqIBg4QlXaS799Pgx1i0RJgSVI1rhiVgToqQ00MlOo50J+lv9MZKBUoV5uJ4wXE3kQ5t4ByrplSvoX+4lJ69y2hZ2uWPZSAZ0NK8SpmZSwzhEVVMnUxUd7IFiJyxTpyjfXki0UK9fUUcgVydXVk6yKiKEOUNTLZDIsjY0m2jqgpT2bhAqIoQyZK1kVZY6Aa09FXorO/zL1bttK2ag3dpQr91ZjBoSq95Sr7+8o8tb+f+3YepL88RH+5OqOXPmPJQ4h1UYZ8NkMuylA3PI0y5LIhhfm6yMhlI+oiGykfZTJEGciYkckYkRkZY3Q+Y2TMRspEYXmislHGMEt+CGaiuun6yXTyuhbyku0k9c3AsJG33cjlQcgfvuYxGLkAslA2mU8qTadc+vppsv2aGbE77j6yLCIiMhkFyDIq1wBtL0jSkVQGk1Hnvk44sIPMgafIlw6RH+ymsdRLW6Uvuc2j0p8E3pUuvNRP3NdLtaePajlDqZShu5Knc2gJfUNNDFaLVKpFqtUCcVwEz+PUAwXiTIFqVGAoylPN1jMU5enLFjiUcTxTIrZDeOa5v51biKjctYsiMPrt0I4RYzaaMpkYMwdLAq7hSMwBJ8mLwzKA2/C60fxkqyHPITaoepIXe1Ku6sl82R3HcTfisIfYfWSb6X07Bja6bsx69zHL49s2fluQfPAwWtbCPsZtY1z9MS/KuLLj58cXn2jdkeoesU4IiL963XWHrRsTK9vkbZ4sb3jZJiuf2qZPMDdabqJ9TR3IjzmOSbc8yes9ft0ku5re9qbaw8THNr5m+qWfemuHr4+rMXbjddMuP9H68ddM03rNYJLXzSddGn/ujO7fxuYfz2u457iv2W5qtVolumny/p2SLoZPOF96+5mcu7Y1WYgPvxXyRKEAWY5OXQGaVyVpxZnTqmJABERxFUo91FcGaKn0c1KlHyoDIZAeSILqoRIMDYbpAHHvQeKunQz29tPb10df/wAD/SUGB0qUBypUBivEg1V80LCSQZwhjrO4Z/A4m+zZs+ARRph6FiMCsmSIiC3CM9kx0ziTxSeapsokf4AtBEIWAtSppuEbN0byMmHKuOXDtze8j8O2O7IuMzKdaHvJP99QxvTNHyInPJ/dAMKOePkwzgyLH12lmZWfcQjsR3UQM9nBjEofn/bP7jHP5H008JHP8PjeO5N6V3xhtpr0nClAluMvE0F9S5KmWyWkxpCOCXeIq+BVKpVByoM9/Oa2WznrjNMYKg8QV8pUhwapDlWIK4MMVSp4pUw1LhNXKlSHysRDQ8RDJeJ4iGp1CIureBzjcRWvViFM3ZM8qnGSN1zO4+QKOo7x4akn48oeD48VO/FIXhiDdkbLeWrsdszER48zfdipQqOjwyHw9vSoZSr4HtlFKm9kGuqnhgk9tfsxeamF9OiojbTYYGSUe/zIq3PYvxJPbzddfvgYR8dVq9WYKIpGyw8fjx++3eH9D4/pHf6nf6LhxiP9m5uqzkTDxxNtb7I8n3S9T7WPw17jycpN1QabYt3R5E11DBNvw93DCOyx2P/M60z8yclE27PUe9YmKDtR3kzM9mipTbiL2Q43p3YUxzyjKhP18bE0g49ujlDlGFaY1e1n1+5i6cpDkF/A3lxultr03ClAlucvM4iyQJa6bJ66+mbyzatZsuasuW6ZzIJNmzaxYcOGuW6GzAL1bW1T/9awTZvmugWT0mesIiIiIiIpCpBFRERERFLmZYBsZheY2ZXd3d1z3RQRERERqTHzMkB29/9x9/c3NzfPdVNEREREpMbMywBZRERERGS2KEAWEREREUlRgCwiIiIikqIAWUREREQkRQGyiIiIiEiKAmQRERERkRQFyCIiIiIiKQqQRURERERSFCCLiIiIiKSYu891G46amXUAT83BrhcDnXOwX5l96tvapb6tXerb2qb+rV1z0bcnuXvbkQrN6wB5rpjZPe5+zly3Q4499W3tUt/WLvVtbVP/1q4TuW91i4WIiIiISIoCZBERERGRFAXIR+fKuW6AzBr1be1S39Yu9W1tU//WrhO2b3UPsoiIiIhIikaQRURERERSFCDPgJm90cweM7NtZnb5XLdHjszMVpvZrWb2iJltMbOPhPxWM7vFzJ4I04Uh38zsa6GPHzKzs1Pbenco/4SZvXuujknGMrPIzO43sxvD8lozuzP04TVmlgv5+bC8Laxfk9rGJ0L+Y2b2hrk5EhnPzFrMbKOZbTWzR83sfJ27tcHMLg1/kzeb2Q/NrKBzd34ys++Y2T4z25zKO2bnqZm9xMweDnW+ZmZ2XA7M3ZWmkYAIaAfWATngQeCUuW6X0hH7bTlwdphvAh4HTgE+D1we8i8HPhfm3wT8H2DAecCdIb8V2B6mC8P8wrk+PiUH+BjwA+DGsPxj4JIw/03gA2H+r4BvhvlLgGvC/CnhfM4Da8N5Hs31cSk5wPeAPw/zOaBF5+78T8BKYAdQH5Z/DLxH5+78TMCrgLOBzam8Y3aeAneFshbq/uHxOC6NIE/fucA2d9/u7mXgR8BFc9wmOQJ33+Pu94X5HuBRkj/OF5H88yVM3xLmLwK+74k7gBYzWw68AbjF3bvc/QBwC/DG43goMgEzWwX8EXBVWDbgNcDGUGR83w73+UbgtaH8RcCP3L3k7juAbSTnu8whM2sm+cf7bQB3L7v7QXTu1oosUG9mWaAI7EHn7rzk7rcBXeOyj8l5GtYtcPc7PImWv5/a1qxSgDx9K4FdqeWnQ57ME+FjubOAO4Gl7r4nrHoWWBrmJ+tn9f+J6SvAZUAclhcBB919KCyn+2mkD8P67lBefXtiWgt0AP8ebqG5yswa0Lk777n7buAKYCdJYNwN3IvO3VpyrM7TlWF+fP6sU4Aszwtm1ghcC3zU3Q+l14WrUn2dyzxjZm8G9rn7vXPdFpkVWZKPbf/V3c8C+kg+qh2hc3d+CvejXkRyEbQCaECj+jVrvp6nCpCnbzewOrW8KuTJCc7M6kiC46vd/bqQvTd8dEOY7gv5k/Wz+v/E83LgQjN7kuSWp9cAXyX5yC4byqT7aaQPw/pmYD/q2xPV08DT7n5nWN5IEjDr3J3/XgfscPcOd68A15Gczzp3a8exOk93h/nx+bNOAfL03Q2cHJ6yzZE8KHDDHLdJjiDcp/Zt4FF3/1Jq1Q3A8FOy7wauT+W/Kzxpex7QHT4mugl4vZktDKMfrw95Mkfc/RPuvsrd15Ccj79093cCtwIXh2Lj+3a4zy8O5T3kXxKelF8LnEzyUIjMIXd/FthlZi8IWa8FHkHnbi3YCZxnZsXwN3q4b3Xu1o5jcp6GdYfM7LzwXnlXaluz63g8CVgrieTpy8dJnpT95Fy3R2laffYKko92HgIeCOlNJPev/QJ4Avg50BrKG/CN0McPA+ektvVekodAtgF/NtfHpjSmnzcw+i0W60j+SW4D/gvIh/xCWN4W1q9L1f9k6PPHOE5PSCtNq1/PBO4J5+9PSJ5u17lbAwn4DLAV2Az8B8k3UejcnYcJ+CHJveQVkk9+3ncsz1PgnPA+aQe+TviRu9lO+iU9EREREZEU3WIhIiIiIpKiAFlEREREJEUBsoiIiIhIigJkEREREZEUBcgiIiIiIikKkEWkZpmZm9kXU8sfN7NPz/I+15jZgJk9kEo5M7vQzC4PZd5iZqfMZjsmadunzWz3uLa1HMV2NpjZjc+hHX87bvn2o92WiMhsUIAsIrWsBLzNzBYf5/22u/uZqVR29xvc/bNh/VuA4x4gB18e17aDc9CGMQGyu79sDtogIjIpBcgiUsuGgCuBS8evMLPvmtnFqeXeMN1gZr8ys+vNbLuZfdbM3mlmd5nZw2a2/mgaYmbvMbOvm9nLgAuBL4QR3PVmtsnMPhf28biZvTLUiczsC2Z2t5k9ZGZ/EfKXm9ltof5mM3tlKPvdsPywmR12zFO07Q4zOzW1vMnMzjGzc83sd2Z2v5ndnvpVu3TdT5vZx1PLm81sTZj/iZnda2ZbzOz9Ie+zQH1o+9Uhb/i1t3C8w8fwjpC/IbRpo5ltNbOrw69qEfrnkfD6XDGjThERmUT2yEVEROa1bwAPmdnnZ1DnDOBFQBewHbjK3c81s48AHwI+eoT6683sgTD/W3f/6+EV7n67md1A8st/GwFCrJcN+3gT8CngdSS/SNXt7i81szzwWzO7GXgbyc+w/qOZRUCR5FfnVrr7aWGbk906camZ/WmYP+Duvw9cA7wd+JSZLQeWu/s9ZrYAeKW7D5nZ64B/Av74yC/fiPe6e5eZ1QN3m9m17n65mX3Q3c+coPzbwnGcASwOdW4L684CTgWeAX4LvNzMHgXeCrzQ3f1obhcREZmIAmQRqWnufsjMvg98GBiYZrW73X0PgJm1AzeH/IeB359G/fZJAsCpXBem9wJrwvzrgRenRrqbgZOBu4HvmFkd8BN3f8DMtgPrzOxfgJ+m2jzel919/Ejrj0P5T5EEyhtT+/uemZ1M8pPtdTM8pg+b2VvD/OrQ9v1TlH8F8EN3rwJ7zexXwEuBQ8Bd7v40QLj4WAPcAQwC3w73RB/1fdEiImm6xUJEng++QjIa25DKGyL8DTSzDJBLrSul5uPUcszsDSwM76Oa2ocBH0rdL7zW3W9299uAVwG7ge+a2bvc/QDJyOsm4C+Bq6a7Y3ffDew3sxcD7yAZUQb4B+DWMCp9AVCYoPrI6xgUILktgmQU/Hx3PwO4f5L605XukyrJiPsQcC5JQP9m4GfPYfsiIiMUIItIzXP3LpJR0velsp8EXhLmL2Tmo6PPRQ/QNI1yNwEfCCPFmNnvmVmDmZ0E7HX3b5EEwmeHBxEz7n4t8HfA2TNs0zXAZUCzuz8U8ppJgnCA90xS78nhfZnZ2cDaVN0D7t5vZi8EzkvVqQwf0zi/Bt4R7qduI7kIuGuyBptZY2jv/5LcZ37GlEcoIjJNCpBF5PniiyT3tQ77FvBqM3sQOB/om8nGLPnatr8/yrb8CPib8PDbVA/9XQU8AtxnZpuBfyMZXd4APGhm95OM+H4VWAlsCrcf/CfwiUm2eamN/Zq3NSF/I3AJyYXEsM8D/xz2M9nI+bVAq5ltAT4IPB7yfwZkw33CnyW5HWLYlST3hV89blv/DTwEPAj8ErjM3Z+dZL+QXGTcaGYPAb8BPjZFWRGRaTN3n+s2iIiIiIicMDSCLCIiIiKSogBZRERERCRFAbKIiIiISIoCZBERERGRFAXIIiIiIiIpCpBFRERERFIUIIuIiIiIpChAFhERERFJ+X9pN6cCzrbPvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(num_problems):\n",
    "    plot_fitness_traces(problem_idx=i, num_runs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compromise\n",
    "\n",
    "When faced with a new problem, we would like to first try and use optimizer parameters that have worked well on many problems in the past. The parameters that we have found using meta-optimization above, were tuned to perform well on multiple problems. This gave us many different choices of parameters with varying performance on the different problems. If we select the parameters that performed best on one problem, then they may perform poorly on another problem.\n",
    "\n",
    "We need to select a good compromise for the parameters. One way of doing this, is to select the parameters that generally perform best on most problems. This can be done by ranking the parameters according to their performance on each problem, and then selecting the parameters whose overall rank is the best.\n",
    "\n",
    "We need a few helper-functions for finding these parameters and plotting their performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_parameters_compromise():\n",
    "    \"\"\"Get the 'best' compromise parameters.\"\"\"\n",
    "    \n",
    "    # We find the 'best' compromise parameters by summing\n",
    "    # their performance ranks on the individual problems.\n",
    "\n",
    "    # The best_parameters array is already sorted\n",
    "    # according to the meta-fitness on the first problem,\n",
    "    # so we just init the ranks with increasing numbers,\n",
    "    # corresponding to the ranks on the first problem.\n",
    "    rank_sum = np.arange(len(best_meta_fitness))\n",
    "\n",
    "    # For each remaining problem.\n",
    "    for i in range(1, num_problems):\n",
    "        # The performance rank for the i'th problem,\n",
    "        # which tells us how well the different parameters\n",
    "        # performed on the i'th problem.\n",
    "        rank = np.argsort(best_meta_fitness[:, i])\n",
    "        \n",
    "        # Add the rank for the i'th problem to the sum.\n",
    "        rank_sum += rank\n",
    "\n",
    "    # The 'best' compromise parameters are the ones that\n",
    "    # have the best (i.e. lowest) rank sum, so find its index.\n",
    "    idx = np.argmin(rank_sum)\n",
    "\n",
    "    # Get those parameters and unpack them.\n",
    "    parameters = unpack_parameters(best_parameters[idx])\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_compromise(num_runs=50):\n",
    "    \"\"\"\n",
    "    Perform the given number of optimization runs with\n",
    "    the 'best' compromise parameters for the DE optimizer\n",
    "    and plot the fitness-traces for the different problems.\n",
    "    \"\"\"\n",
    "\n",
    "    # Create a single plot.\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(211)\n",
    "\n",
    "    # Get the 'best' compromise parameters.\n",
    "    params = get_parameters_compromise()\n",
    "\n",
    "    # Print the parameters.\n",
    "    print(\"Parameter Compromise:\")\n",
    "    print_parameters_raw(params=params)\n",
    "\n",
    "    for problem in problems:\n",
    "        # Fitness-trace for the compromise parameters.\n",
    "        trace, steps = \\\n",
    "            multi_fitness_traces(params=params,\n",
    "                                 problem=problem,\n",
    "                                 num_runs=num_runs)\n",
    "\n",
    "        # Plot the fitness traces.\n",
    "        ax.plot(steps, trace, label=problem.get_name())\n",
    "    \n",
    "    # Set y-axis to log-scale.\n",
    "    ax.set_yscale(\"log\")\n",
    "    \n",
    "    # Set plot title and labels.\n",
    "    ax.set_title(\"DE Parameter Compromise\")\n",
    "    ax.set_xlabel(\"Num. Fitness Evaluations\")\n",
    "    ax.set_ylabel(\"Mean Fitness (Log-Scale)\")\n",
    "    \n",
    "    # Show legend.\n",
    "    ax.legend()\n",
    "    \n",
    "    # Show grid.\n",
    "    ax.grid()\n",
    "    \n",
    "    # Plot with a compact layout.\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the performance of the 'best' compromise parameters, which perform well overall on the given benchmark problems. But it is possible to find parameters that perform better on individual problems at the cost of worse performance on the other problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter Compromise:\n",
      "pop_size: 54\n",
      "CR: 0.91\n",
      "F: 0.51\n",
      "variant: 4 (best/2/exp)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compromise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this paper we used multi-objective meta-optimization to tune the control parameters of Differential Evolution (DE) on several benchmark problems. This resulted in a list of DE parameters that had varying performance on the individual problems.\n",
    "\n",
    "We then found a set of compromise parameters that performed well overall, but were perhaps not the best choice for any individual problem. These parameters would be a good default choice of parameters for the DE optimizer when trying to optimize a new problem.\n",
    "\n",
    "This meta-optimization method can also be used when developing new optimizers to rigorously test the optimizer. You can also use the method to find good default choices of parameters tuned for several benchmark problems and long / short optimization runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises & Research Ideas\n",
    "\n",
    "There are plenty of opportunities for doing original research using these techniques. Here are some ideas to get you started:\n",
    "\n",
    "- Try running more iterations of the meta-optimizer. Does that improve the results?\n",
    "\n",
    "- Tune the parameters of another optimizer such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA).\n",
    "\n",
    "- Compare the parameters we found above using meta-optimization to some standard parameters recommended in the research literature. Also try and compare the performance on benchmark problems for which the parameters weren't tuned.\n",
    "\n",
    "- Try using many more benchmark problems in meta-optimization, e.g. 10, 20 or 30 problems. Note that the NSGA-2 method was only intended for 2-3 objectives, which is called *multi-objective* optimization. Having 4 or more objectives is called *many-objective* optimization which poses challenges that NSGA-2 may not handle so well. You may need to use NSGA-3 or another *many-objective* optimizer to handle that many objectives, but these are currently not implemented in Pygmo.\n",
    "\n",
    "- Plot the Pareto front using the source-code from the [second paper](https://github.com/Hvass-Labs/MetaOps/blob/master/02_Multi-Objective_Meta-Optimization.ipynb). It does not work so well for 3 or more objectives. Can you find a better way to plot the Pareto front, or another way of visualizing the results?\n",
    "\n",
    "- In addition to using multiple benchmark problems, also try and use objectives for both longer and shorter optimization runs for those problems.\n",
    "\n",
    "- Instead of using `@lru_cache` on the `meta_fitness()` function, make your own cache-function which first performs e.g. 8 optimization runs and then returns the average fitness of those runs. On the next call it performs another 8 runs and returns the mean of the total 16 runs, and so forth until e.g. 128 runs have been performed. This may both speed up the meta-optimization as well as improve the accuracy, because poor choices of DE parameters will have few runs, while good choices of parameters will have many runs to accurately distinguish similar parameters from each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}