Public reference body for agent-owned software, co-evolution, and Genesis descent.
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Updated
Sep 29, 2026 - Python
Public reference body for agent-owned software, co-evolution, and Genesis descent.
Co-evolution of agents and environments in GVG-AI
Code used in the experiments of the paper COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks http://gecco-2019.sigevo.org
Culebra is a versatile and extensible Python framework for solving complex optimization problems using metaheuristics.
Creation engine for agent-owned software bodies, reproducible descent, and independent lineage.
Optimization of organic chemistry catalysts using the coevolution approach
COCOA-Tree: COllaborative COevolution Analysis Toolbox
A command-line pipeline for predicting and characterising protein-protein interactions using AlphaFold3
High-throughput coevolutionary chess AI. Rust simulation core + Python harness: NEAT/NSGA-II/MAP-Elites, W&B sweeps, Lichess bot, Docker, CI.
Agent-based model (ABM) of trait–preference coevolution and sexual selection in a resource-limited 2D environment (Mesa + Solara).
Genetic algorithm experiments from The Blind Watchmaker: the weasel program and a host/parasite coevolution simulator, with each condition measured over repeated runs.
Advanced Method of Optimization (2022 Spring)
Repository on my course Evolutionary Computation, about Genetic Algorithms. This project has the objective of evolving structures and controllers on evogym
Co-evolutionary framework pitting offensive and defensive AI agents against each other on a simulated corporate network using MITRE ATT&CK techniques and NSGA-II multi-objective optimization.
Reference implementation and experiment battery for the Geno-Synthetic Algorithm (GSA), a type-factored coevolutionary optimizer for heterogeneous genotypes.
An evolution simulation whose only law of nature is an AI judgment model's yes/no answers. Islands evolve what common sense expects; a predator-prey arms race appears only when predators alone select prey, tested against no-interaction, fixed-predator and shuffled controls.
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