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Represents solutions as tree structures and applies crossover, mutation, and selection to evolve them over generations.
Distinct from Tree-Based Optimization: Distinct from general Tree-Based Optimization: specifically for evolving programs via genetic operators, not for DP-based tree aggregation.
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Represents solutions as tree structures and evolves them using crossover, mutation, and selection.
OpenEvolve is an open-source framework for evolutionary computation that uses language models to drive automated optimization across multiple domains. It can evolve system prompts for large language models, refine source code across programming languages, search for optimal GPU kernel configurations, discover interpretable mathematical expressions from data, and maintain diverse populations of high-performing solutions. The framework integrates multiple evolutionary strategies, including MAP-Elites diversity mapping and island-based topologies, to avoid premature convergence and preserve a wid
Evo evolves a program that passes all test cases on an online judge by iteratively mutating and selecting candidate solutions.
Evolve is an evolution-based organism designer and GPU-accelerated artificial life simulator that combines interactive particle physics with a real-time simulation editor. At its core, it runs genetic algorithm evolution on self-replicating graph structures to evolve digital organisms, offloading particle physics, neural networks, and rendering entirely to the GPU through a compute shader pipeline for real-time performance. The project distinguishes itself with graph-based organism design that uses a directed graph editor to visually define organism structure, connections, and neural controll
Evolves digital organisms with neural networks and genetic algorithms to develop complex behaviors without manual programming.