source code from the book Genetic Algorithms with Python by Clinton Sheppard
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Updated
Oct 17, 2022 - Python
source code from the book Genetic Algorithms with Python by Clinton Sheppard
PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their *Large-Scale* versions/variants (evolutionary algorithms/swarm-based optimizers/pattern search/...). [https://pypop.rtfd.io/]
A 2D/3D visualization of the Traveling Salesman Problem main heuristics
A pytorch implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm
AI research environment for the game of Snake 🐍 .
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Evolve complex cellular automata with a genetic algorithm.
Python implementation for TSP using Genetic Algorithms, Simulated Annealing, PSO (Particle Swarm Optimization), Dynamic Programming, Brute Force, Greedy and Divide and Conquer
🧬 Modularised Evolutionary Algorithms For Python with Optional JIT and Multiprocessing (Ray) support. Inspired by PyTorch Lightning
Evolutionary Algorithm for the 2D Packing Problem combined with the 0/1 Knapsack Problem (Master Thesis)
EasyGA is a python package designed to provide an easy-to-use Genetic Algorithm. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit.
Biologically-Inspired and Machine Learning Algorithms written in Python
Supported highly optimized and flexible genetic algorithm package for python3.8+
"Using Genetic Algorithms for Multi-depot Vehicle Routing" paper implementation.
Usage of genetic algorithms to train a neural network in multiple OpenAI gym environments.
🐤The next evolution of, well, evolution.
Knapsack Problem solved using Genetic optimization algorithm
Neuroevolution framework for Python.
An implementation of various metaheuristics adapted to train neural networks
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