Modular framework for evolutionary algorithms and neuroevolution

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.

A bare-bones Python library for quality diversity optimization.

Fahlevisia Website

Open-source implementation of AlphaEvolve

A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

An evolutionary algorithm library in Java

Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.

A collection of MetaBBO papers and code sources

(In progress) C++ library for genetic algorithm based on binary individuals.

Evolutionary-Guided Advanced Deep Learning Architecture Powers Mammalian GPCRome Agonist Predictions

Exploring the creative potential of applying sequences of operations to typography through Evolutionary Computation

A Rust framework supporting a variety of evolutionary computation (EC) tools

(ancient german = improving, rearranging, rendering benign)

manifest destiny

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

This project is an interactive demo of the generative graph unfolding cellular automata (GUCA), visualizing morphogenesis. The "gene" of the growing graph is a program of primitive operations, selected either manually or by a genetic algorithm (evolution). Users can select the "gene" and manipulate the "living" graph during simulations.

Creating an artificial, open-ended universe bottom-up.

This repository is used for the assessor of the module: Evolutionary Computations, to see how my solution to this problem has evolved overtime.

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