Evolutionary Computation
Showing 20 of 20 projects
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.
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 Clojure implementation of the Push programming language and PushGP genetic programming system for evolutionary computation.
A fast and flexible Rust library for implementing genetic algorithms, neuroevolution, and genetic programming.
A flexible genetic algorithm library for Go, enabling optimization and simulation through configurable components.
A library for programmatic modification and evaluation of software across source code, assembly, and binary formats.
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.
An Elixir framework for evolutive neural networks with a modular DSL and OTP-based architecture.
A modular framework for executing genetic algorithms in Rust with a simple API.
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 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.
A genetic algorithm library for Ada with binary chromosomes, elitism, mutation, and crossover operations.
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