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README.md

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An open-source implementation of the AlphaEvolve system described in the Google DeepMind paper "AlphaEvolve: A coding agent for scientific and algorithmic discovery" (2025).
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![OpenEvolve Logo](openevolve-logo.png)
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## Overview
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OpenEvolve is an evolutionary coding agent that uses Large Language Models to optimize code through an iterative process. It orchestrates a pipeline of LLM-based code generation, evaluation, and selection to continuously improve programs for a variety of tasks.
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OpenEvolve follows an evolutionary approach with the following components:
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![OpenEvolve Architecture](openevolve-architecture.png)
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1. **Prompt Sampler**: Creates context-rich prompts containing past programs, their scores, and problem descriptions
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2. **LLM Ensemble**: Generates code modifications via an ensemble of language models
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3. **Evaluator Pool**: Tests generated programs and assigns scores
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4. **Program Database**: Stores programs and their evaluation metrics, guiding future evolution
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The controller orchestrates interactions between these components in an asynchronous pipeline, maximizing throughput to evaluate as many candidate solutions as possible.
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## Getting Started
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### Installation
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See the `examples/` directory for complete examples of using OpenEvolve on various problems:
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### Circle Packing
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Our implementation of the circle packing problem from the AlphaEvolve paper, where we successfully match their reported results within 0.04%.
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[Explore the Circle Packing Example](examples/circle_packing/)
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### Function Minimization
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An example showing how OpenEvolve can transform a simple random search algorithm into a sophisticated simulated annealing approach.
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[Explore the Function Minimization Example](examples/function_minimization/)
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## Preparing Your Own Problems
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To use OpenEvolve for your own problems:
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openevolve-architecture.png

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openevolve-logo.png

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