Open-source implementation of AlphaEvolve

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

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

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)

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

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

A GPU-accelerated library for Tree-based Genetic Programming, leveraging PyTorch and custom CUDA kernels for high-performance evolutionary computation. It supports symbolic regression, classification, and policy optimization with advanced features like multi-output trees and benchmark tools.

Tiny Genetic Programming in Python.

EC-KitY: A scikit-learn-compatible Python tool kit for doing evolutionary computation.

EvoMO is a GPU-accelerated library for evolutionary multiobjective optimization (EMO)

Push Genetic Programming in Python.

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

Adaptable tools to make reinforcement learning and evolutionary computation algorithms.

This course covers the applied side of algorithmics in machine learning, with some deep learning and evolutionary algorithms thrown in as well.

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

Symbolic Generators for Complex Networks