Genetic Algorithm
Showing 26 of 26 projects
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
A Python library for heuristic optimization algorithms including Genetic Algorithm, PSO, Simulated Annealing, and Ant Colony Optimization.
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
A minimal, well-tested library for training and using feedforward artificial neural networks in ANSI C.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A fast, extensible, multi-platform C# library for implementing genetic algorithms in .NET applications.
An evolutionary optimization library for Go implementing genetic algorithms, particle swarm optimization, differential evolution, and other algorithms.
A Python framework for gradient-free optimization, featuring common algorithms like genetic algorithms and simulated annealing.
A genetic algorithm for optimizing trading strategies in the Gekko cryptocurrency trading bot.
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.
A flexible genetic algorithm library for Go, enabling optimization and simulation through configurable components.
A Node.js native library that uses genetic algorithms to perform technical analysis on OHLC data and generate profitable trading strategies.
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.
An Elixir framework for evolutive neural networks with a modular DSL and OTP-based architecture.
A Go implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm for evolving neural network structures.
An extensible .NET genetic algorithm library for optimization and AI, making evolutionary computation simple.
A Scala machine learning library with simple, readable implementations of classic algorithms for prototyping and education.
A Go library implementing Genetic Algorithm and Particle Swarm Optimization for solving optimization problems.
A metaheuristic optimization framework for solving complex combinatorial problems like N-queens and protein folding.
A Swift framework for building fast and extensible genetic algorithms with support for multiple platforms.
Automatically evolves profitable trading strategies using genetic algorithms across 484 factors with walk-forward validation for crypto, A-shares, and US stocks.
A simple Ruby implementation of a genetic algorithm for solving the 1-max problem, designed for educational demonstrations.
A genetic algorithm library for Ada with binary chromosomes, elitism, mutation, and crossover operations.
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