In this work, we introduce CodeEvolve, an open-source evolutionary coding agent that unites Large Language Models (LLMs) with genetic algorithms to solve complex computational problems. Our framework adapts powerful evolutionary concepts to the LLM domain, building upon recent methods for generalized scientific discovery. CodeEvolve employs an island-based genetic algorithm to maintain population diversity and increase throughput, introduces a novel inspiration-based crossover mechanism that leverages the LLMs context window to combine features from successful solutions, and implements meta-prompting strategies for dynamic exploration of the solution space. We conduct a rigorous evaluation of CodeEvolve on a subset of the mathematical benchmarks used to evaluate Google DeepMind's closed-source AlphaEvolve. Our findings show that our method surpasses AlphaEvolve's performance on several challenging problems. To foster collaboration and accelerate progress, we release our complete framework as an open-source repository.

翻译：本文介绍CodeEvolve，一种将大型语言模型（LLMs）与遗传算法相结合以解决复杂计算问题的开源进化编码智能体。我们的框架基于近期广义科学发现的方法，将强大的进化概念适配到LLM领域。CodeEvolve采用基于岛屿的遗传算法以维持种群多样性并提高吞吐量，引入了一种新颖的基于启发的交叉机制，该机制利用LLM的上下文窗口融合成功解决方案的特征，并实现了元提示策略以动态探索解空间。我们在用于评估Google DeepMind闭源AlphaEvolve的数学基准子集上对CodeEvolve进行了严格评估。研究结果表明，我们的方法在多个具有挑战性的问题上超越了AlphaEvolve的性能。为促进协作并加速进展，我们将完整框架作为开源仓库发布。