Popular GitHub Repositories for Crossover

:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js

MetaDE is a GPU-accelerated evolutionary framework that optimizes Differential Evolution (DE) strategies via meta-level evolution. Supporting both JAX and PyTorch, it dynamically adapts mutation and crossover strategies for efficient large-scale black-box optimization.

This is the repository of codes written in class.

TSDE is a novel SSL framework for TSRL, the first of its kind, effectively harnessing a diffusion process, conditioned on an innovative dual-orthogonal Transformer encoder architecture with a crossover mechanism, and employing a unique IIF mask strategy (KDD 2024, main research track).

GeneticPromptLab uses genetic algorithms for automated prompt engineering (for LLMs), enhancing quality and diversity through iterative selection, crossover, and mutation, while efficiently exploring minimal yet diverse samples from the training set.

GARNE: Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

An Elixir library for genetic programming and neuroevolution, implementing standard genetic algorithms and NEAT. Features parallel evaluation, checkpointing, and extensible operators for selection, crossover, and mutation. Solve optimization problems through evolution with a clean, flexible API.

Neural Evolution of Augmenting Topologies(NEAT) library written in javascript, heavily based on Kenneth O. Stanley's c++ library.

An experimental evolutionary music generator that uses perfect shuffle crossover to create evolving MIDI sequences

Backtesting trading strategy performances between actual market returns, a dual moving average crossover strategy and support vector machines

Series of simple algorithmic trading experiments implemented in Python. Starting with basic strategies (Golden Cross, etc.) on SPY for educational purposes. I'm a beginner in Python and using this repo to learn and share progress.

Comparison of Crossover operators of Genetic Algorithms in combinatorial optimization problems

In this project, I implemented genetic algorithm (GA) from scratch using python to select the most impactful features in a dataset.

GeneticFlapAI is an interactive project that demonstrates how genetic algorithms can evolve neural network controllers for game agents. Follow along as populations of Flappy Bird–style agents adapt and improve through mutation and crossover over successive generations.