We introduce CodeEvolve, an open-source framework that combines large language models (LLMs) with evolutionary search to synthesize high-performing algorithmic solutions. CodeEvolve couples an islands-based genetic algorithm with modular LLM orchestration, using execution feedback and task-specific metrics to guide selection and variation. Exploration and exploitation are balanced through context-aware recombination, adaptive meta-prompting, and targeted refinement of promising solutions. We evaluate CodeEvolve on benchmarks previously used to assess Google DeepMind's AlphaEvolve, showing superior performance on several tasks and competitive results overall. Notably, open-weight models often match or exceed closed-source baselines at a fraction of the compute cost. We provide extensive ablations analyzing the contribution of each component and release our framework and experimental results at https://github.com/inter-co/science-codeevolve.

翻译：本文介绍CodeEvolve，这是一个结合大型语言模型（LLMs）与进化搜索以合成高性能算法解决方案的开源框架。CodeEvolve将基于岛屿模型的遗传算法与模块化LLM编排相耦合，利用执行反馈和任务特定指标来指导选择与变异过程。通过上下文感知重组、自适应元提示以及对有潜力解决方案的定向精化，实现了探索与利用的平衡。我们在曾用于评估Google DeepMind的AlphaEvolve的基准测试上对CodeEvolve进行评估，结果显示其在多项任务上具有优越性能，整体表现具有竞争力。值得注意的是，开源模型仅需少量计算成本，其性能即可匹配甚至超越闭源基线模型。我们提供了详尽的消融实验以分析各组件的贡献，并将框架及实验结果发布于https://github.com/inter-co/science-codeevolve。