The Self-Evolving Agent Ecosystem — Trading agents that evolve through Darwinian selection and adversarial self-play
An evolutionary computation framework for Ruby
Generating Evolutionary Opponents as a Reinforcement Guided Exploration Solution
Projects and assignments for the Artificial Intelligence Techniques to Control systems course at UCM, with topics such as: Smart control, Expert systems, Neural Networks, Logic Diffuse, Evolutionary computing, Intelligent agents
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.
High-performance Evolutionary TSP Solver with Gamified Streamlit UI. Features Hybrid GA (Genetic Algorithm) + 3-Opt Local Search, TSPLIB support (EUC_2D/ATT), and real-time visualization.
A pure-MATLAB library of EVolutionary (population-based) OPTimization for Large-Scale black-box continuous Optimization (evopt-lso).
This repository includes all my notes material about Evolutionary Computing lesson. Those notes based from Introduction to Evolutionary Computing 2nd Edition book.
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.
Evolutionary solution to the N-Queens problem.
An evolutionary algorithm library in Java
A high-performance Python implementation of Dawkin's Weasel experiment. Uses multiprocessing and matplotlib to visualise how different mutation rates affect evolutionary convergence speed.
Central dogma of biology as map for Prompt→Loop→Graph→Evolution trajectory of AI agents. Skill framework, research scaffold, practical patterns.
A block-based neural network trained by genetic algorithm
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 dynamic visualization of the Travelling Salesman Problem designed to explore evolutionary computation.
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