GigaEvo：一个由大语言模型与进化算法驱动的开源优化框架

GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms

November 17, 2025

作者: Valentin Khrulkov, Andrey Galichin, Denis Bashkirov, Dmitry Vinichenko, Oleg Travkin, Roman Alferov, Andrey Kuznetsov, Ivan Oseledets

cs.AI

摘要

近期，在LLM引导的进化计算领域，尤其是AlphaEvolve（Novikov等人，2025；Georgiev等人，2025）的研究中，取得了显著进展，成功发现了新颖的数学构造并解决了复杂的优化问题。然而，已发表的工作中高层次描述未明确许多实现细节，阻碍了研究的可重复性和进一步探索。本报告介绍了GigaEvo，一个可扩展的开源框架，旨在让研究人员能够研究和实验受AlphaEvolve启发的混合LLM-进化方法。我们的系统提供了关键组件的模块化实现：MAP-Elites质量多样性算法、基于异步DAG的评估管道、具备洞察生成与双向谱系跟踪的LLM驱动变异算子，以及灵活的多岛进化策略。为了评估可重复性并验证我们的实现，我们在AlphaEvolve论文中的挑战性问题——海伦三角形放置、正方形内圆填充及高维接吻数问题上对GigaEvo进行了测试。该框架强调模块化、并发性和实验便捷性，通过声明式配置实现快速原型设计。我们详细描述了系统架构、实现决策和实验方法，以支持LLM驱动进化方法的进一步研究。GigaEvo框架及所有实验代码可在https://github.com/AIRI-Institute/gigaevo-core获取。

English

Recent advances in LLM-guided evolutionary computation, particularly AlphaEvolve (Novikov et al., 2025; Georgiev et al., 2025), have demonstrated remarkable success in discovering novel mathematical constructions and solving challenging optimization problems. However, the high-level descriptions in published work leave many implementation details unspecified, hindering reproducibility and further research. In this report we present GigaEvo, an extensible open-source framework that enables researchers to study and experiment with hybrid LLM-evolution approaches inspired by AlphaEvolve. Our system provides modular implementations of key components: MAP-Elites quality-diversity algorithms, asynchronous DAG-based evaluation pipelines, LLM-driven mutation operators with insight generation and bidirectional lineage tracking, and flexible multi-island evolutionary strategies. In order to assess reproducibility and validate our implementation we evaluate GigaEvo on challenging problems from the AlphaEvolve paper: Heilbronn triangle placement, circle packing in squares, and high-dimensional kissing numbers. The framework emphasizes modularity, concurrency, and ease of experimentation, enabling rapid prototyping through declarative configuration. We provide detailed descriptions of system architecture, implementation decisions, and experimental methodology to support further research in LLM driven evolutionary methods. The GigaEvo framework and all experimental code are available at https://github.com/AIRI-Institute/gigaevo-core.