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

Evolutionary Peer Instruction Environment

Evolutionary Computation: A Modern Perspective ---> This is a free online book, which is actively updated now (from 2023 to 2027).

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

Source code for "Evolution of Adaptive Force Chains in Reconfigurable Granular Metamaterials"

Code for "Evolving Variable Stiffness Fiber Patterns for Multi-shape Robotic Sheets", Parsa et al., Robosoft 2023

PhD Candidate in Artificial Intelligence. Applying machine learning to fish oil analysis.

Fahlevisia Website

This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python.

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

Python phylogeny tracker for in silico evolution experiments

Push Genetic Programming in Python.

A bare-bones Python library for quality diversity optimization.

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

This repositories include the IEEE Congress on Evolutionary Computation Benchmark functions suite (IEEE CEC 2014 2017 2020 2022). You can use the untitled.m to form a figure of the benchmark function.

An evolutionary algorithm library in C++

A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data.

This repository provides practical implementations, examples, and insights into various optimization methods, making it easier to understand and apply these concepts.