The Self-Evolving Agent Ecosystem — Trading agents that evolve through Darwinian selection and adversarial self-play

Generating Evolutionary Opponents as a Reinforcement Guided Exploration Solution

GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network

Transfer learning on head pose estimation problem enhanced by neural architecture search.

A comprehensive collection of evolutionary AI projects built with NEAT algorithm showcasing intelligent behavior development in Car, Snake, and Pong games.

Asteroids game with AI agents trained over generations using neural networks and a genetic algorithm. Built with Pygame.

Solving N-Queens problem by Simple Genetic Algorithm in Python.

Python tool that uses seeded and non-seeded genetic algorithms to generate date-validation test cases, measure category coverage, and export evolved test suites to CSV.

A high-performance Python implementation of Dawkin's Weasel experiment. Uses multiprocessing and matplotlib to visualise how different mutation rates affect evolutionary convergence speed.

A Python implementation of the Particle Swarm Optimization (PSO) algorithm for continuous optimization problems. The project demonstrates swarm-based search using the Rastrigin and Shifted Griewank benchmark functions and includes real-time 2D visualization of particle movement, velocity vectors, and convergence toward the global optimum.

A high-performance optimization engine designed to solve complex combinatorial problems. This solver implements custom selection, crossover, and mutation strategies to evolve optimal solutions for tasks like scheduling, resource allocation, and pathfinding.