Genetic Algorithms
Showing 28 of 28 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.
A fast, extensible, multi-platform C# library for implementing genetic algorithms in .NET applications.
Source code and tutorials for learning genetic algorithms and genetic programming in Python through hands-on example projects.
An evolutionary optimization library for Go implementing genetic algorithms, particle swarm optimization, differential evolution, and other algorithms.
A comprehensive Swift framework providing AI/ML algorithms including neural networks, SVMs, PCA, genetic algorithms, and MDPs with GPU acceleration support.
A comprehensive Swift framework providing AI/ML algorithms including neural networks, SVMs, genetic algorithms, and MDPs with GPU acceleration.
A comprehensive, high-performance library implementing 30+ Evolution Strategies in JAX for scalable optimization on modern hardware.
A scikit-learn compatible hyperparameter optimization tool using evolutionary algorithms instead of grid search.
A lightweight Ruby playground with clean, readable implementations of core AI algorithms for learning and experimentation.
A lightweight Ruby playground with clean implementations of core AI algorithms for learning and experimentation.
A Python framework for multiobjective evolutionary algorithms (MOEAs) with support for NSGA-II, NSGA-III, MOEA/D, and other optimization methods.
A fast and flexible Rust library for implementing genetic algorithms, neuroevolution, and genetic programming.
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 fast, parallel, and extensible genetic algorithms framework implemented in Rust for solving optimization problems.
A neuroevolution-based trading bot that evolves populations of neural networks to trade cryptocurrency using technical analysis.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
A Clojure wrapper for the Encog machine learning framework, specializing in neural network construction and training.
A Rust library for writing evolutionary algorithms to solve optimization problems like TSP, Sudoku, and OCR.
A modular framework for executing genetic algorithms in Rust with a simple API.
A Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms.
An extensible .NET genetic algorithm library for optimization and AI, making evolutionary computation simple.
A Python library implementing biologically-inspired algorithms including neural networks, genetic programming, and particle swarm optimization.
Automatically evolves profitable trading strategies using genetic algorithms across 484 factors with walk-forward validation for crypto, A-shares, and US stocks.
A Swift framework for building fast and extensible genetic algorithms with support for multiple platforms.
Generates nutritionally complete DIY soylent recipes using genetic algorithms to optimize ingredient combinations.
A genetic algorithm library for Ada with binary chromosomes, elitism, mutation, and crossover operations.
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