openevolve

ork together in an evolutionary loop: 1. Program Database: Stores programs and their metrics in a MAP-Elites inspired structure 2. Prompt Sampler: Creates context-rich prompts with past solutions 3. LLM Ensemble: Generates code modifications using multiple models 4. Evaluator Pool: Tests programs and provides feedback metrics What you can do with it: - Run existing examples to see evolution in action - Define your own problems with custom evaluation functions - Configure LLM backends (works with any OpenAI-compatible API) - Use multiple LLMs in ensemble for better results - Optimize algorithms with multiple objectives Two examples I've replicated from the AlphaEvolve paper: - Circle Packing: Evolved from simple geometric patterns to sophisticated mathematical optimization, reaching 99.97% of DeepMind's reported results (2.634 vs 2.635 sum of radii for n=26). - Function Minimization: Transformed a random search into a complete simulated annealing algorithm with cooling schedules and adaptive step sizes. Technical insights: - Low latency LLMs are critical for rapid generation cycles - Best results using Gemini-Flash-2.0-lite + Gemini-Flash-2.0 as the ensemble - For the circle packing problem, Gemini-Flash-2.0 + Claude-Sonnet-3.7 performed best - Cerebras AI's API provided the fastest inference speeds - Two-phase approach (exploration then exploitation) worked best for complex problems Getting started (takes < 2 minutes) # Clone and install git clone https://github.com/codelion/openevolve.git cd openevolve pip install -e . # Run the function minimization example python openevolve-run.py examples/function_minimization/initial_program.py \ examples/function_minimization/evaluator.py \ --config examples/function_minimization/config.yaml \ --iterations 50 All you need is Python 3.9+ and an API key for an LLM service. Configuration is done through simple YAML files. I'll be around to answer questions and discuss!