Applications of Metaheuristic Optimization in Python
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Updated
May 18, 2024 - Python
Applications of Metaheuristic Optimization in Python
Heuristics for the Quadratic Assignment Problem (QAP) - R package
A research protocol for deep graph matching.
Unofficial implemnetation of "Solving Quadratic Assignemt Problem using Deep Reinforcement Learning" (https://arxiv.org/abs/2310.01604)
A simple Quadratic Assignment Problem solver using heuristics and metaheuristics
Code and experiment data from the paper: "Cycle Mutation: Evolving Permutations via Cycle Induction"
Product Positioning via multidimensional scaling, using R to perform QAP correlation testing
This Rust program accepts command-line arguments and functions as a quadratic equation solver, akin to the 'Almighty Formula.' It can solve any quadratic equation of the form ax^2 + bx + c = 0, provided that you correctly pass the expected arguments.
This is the official code for the AISTATS 2023 paper "Learning Constrained Structured Spaces with Application to Multi-Graph Matching"
Python library for finding the optimal transformation(s) that makes two matrices as close as possible to each other.
This package supports general, orthogonal, rotation, permutation, projection, and symmetric Procrustes problems, including both the normal one-sided approach and (for orthogonal and permutation Procrustes) two-sided approaches, where both the rows and columns are transformed.
Testing three Genetic Algorithm on three datasets to check their performance.
Solving quadratic assignment problem using iterated local search, improved hybrid genetic algorithm, tabu search, and constraint solving.
Solving a QAP instance using genetic algorithms
Genetic algorithm for solving quadratic assignment problem
Quadratic Assignment Problem, an Enhanced Solver
Repo de la materia de TSO, trae cosas de Knapsack, TSP y Quadratic Assignment Problem
Algorithms for solving the quadratic assignment problem (QAP) implemented in Julia.
This repository utilizes social network analysis (SNA) to analyze multiple social networks. Clustering algorithms were used to explore sub-groups and QAP was implemented for non-parametric multiple regression analysis.
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