Evolutionary Algorithms
Showing 31 of 31 projects
A Python framework for rapid prototyping and testing of evolutionary algorithms, including genetic algorithms, genetic programming, and evolution strategies.
A Python framework for rapid prototyping and testing of evolutionary algorithms, including genetic algorithms, genetic programming, and evolution strategies.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
A JAX-based library providing accelerated reinforcement learning environments with full compatibility to the classic gym API.
An evolutionary optimization library for Go implementing genetic algorithms, particle swarm optimization, differential evolution, and other algorithms.
A scikit-learn compatible hyperparameter optimization tool using evolutionary algorithms instead of grid search.
Automated modeling and machine learning framework FEDOT
A Python framework for multiobjective evolutionary algorithms (MOEAs) with support for NSGA-II, NSGA-III, MOEA/D, and other optimization methods.
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A hardware-accelerated Python library for running Quality-Diversity and neuroevolution algorithms in minutes instead of days.
A decentralized hyperparameter optimization framework for Go, inspired by Optuna, supporting Bayesian optimization and evolution strategies.
A fast Evolution Strategy implementation in Python
A fast and flexible Rust library for implementing genetic algorithms, neuroevolution, and genetic programming.
A Python library for feature selection using nature-inspired wrapper algorithms like particle swarm, grey wolf, and genetic optimization.
An artificial life simulation system that evolves neural networks for artificial intelligence research.
A flexible Rust framework for building and running genetic algorithm simulations for optimization and search problems.
A genetic programming platform for Python with TensorFlow for fast CPU and GPU symbolic regression and classification.
A neuroevolution-based trading bot that evolves populations of neural networks to trade cryptocurrency using technical analysis.
A Rust library for writing evolutionary algorithms to solve optimization problems like TSP, Sudoku, and OCR.
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
A strongly-typed genetic programming framework for Python that makes evolutionary algorithms accessible and fun.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A genetic programming library for Clojure that evolves programs using mutation, reproduction, and fitness functions.
A Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms.
A Common Lisp library implementing evolutionary algorithms including Genetic Programming and Differential Evolution for optimization tasks.
A Go library implementing Genetic Algorithm and Particle Swarm Optimization for solving optimization problems.
A Swift framework for building fast and extensible genetic algorithms with support for multiple platforms.
A comprehensive Ruby gem providing a collection of atomic machine learning tools and frameworks for practical applications.
A comprehensive Ruby gem providing atomic, flexible tools and frameworks for practical machine learning applications.
A simple Ruby implementation of a genetic algorithm for solving the 1-max problem, designed for educational demonstrations.
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