Neuroevolution for Single Layer Perceptron

Introduction

This is my implementation of using Genetic Algorithm to seek for good hyperparameters for Single Layer Perceptron. Method of using GA with ANN is called Neuroevolution.

The hyperparameters in this implementation are :

1. Initial bias
2. Alpha (learning rate)
3. Theta (perceptron's activation function's threshold)

Those hyperparameters became the chromosome's genes.

The fitness function's value corresponds to the amount of epochs a perceptron required to be able to predict the targets correctly. The lower the epochs the better the fitness value is.

Modification

The implemented Single Layers Perceptron is able to do Linear Classification with any number of input neurons.

If you want to modify the number of input neurons, set the neurons parameter on Perceptron or NeuroEvolution's constructor to the desired amount. Also set the elements of data list with lists that have the same amount of elements as neurons parameter.

The default data and targets variables are set up to train perceptron to recognize three inputs AND boolean operator.

You could also modify the population_count, epochs, mutation_rate, max_iteration parameter for neuroevolution.

Running

Just execute the file with python neuroevolution-perceptron.py, easy peasy. You could choose to run a single perceptron process by calling run_single_perceptron() method or the entire neuroevolution process by calling run_neuro_evolution() method.

Dependency

Nothing.

Todo

• Modifying the fitness function to be able to consider the perceptron's accuracy.
• Using argv to change which method to run.