Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.

🍀 Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)

A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

SharpNEAT - Evolution of Neural Networks. A C# .NET Framework.

NSGA-Net, a Neural Architecture Search Algorithm

A bare-bones Python library for quality diversity optimization.

A framework for gene expression programming (an evolutionary algorithm) in Python

Flappy Bird AI using Cartesian Genetic Programming (Evolutionary Computation)

Return Oriented Programme Evolution with ROPER

Programmatic modification and evaluation of software

A lightweight, generic C++11 framework for evolutionary computation

Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

Evoplex is a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. It's available for Windows, Linux and macOS.

talk-generator is capable of generating coherent slide decks based on a single topic suggestion.

A (still growing) paper list of Evolutionary Computation (EC) published in some (rather all) top-tier (and also EC-focused) journals and conferences. For EC-focused publications, only Parallel/Distributed EC are covered in the current version.

Tiny Genetic Programming in Python

EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.

Modular Agent Based Evolution Framework

Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.

A public python implementation of the DeepHyperNEAT system for evolving neural networks. Developed by Felix Sosa and Kenneth Stanley. See paper here: https://eplex.cs.ucf.edu/papers/sosa_ugrad_report18.pdf