Paper 2511.17592
GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms
- Published
- Nov 2025
- Research lab
- Independent
- Citations
- 13
- GitHub
- 125 stars
01 In brief
Summary
GigaEvo is an open-source framework for LLM-driven evolutionary computation, inspired by AlphaEvolve, designed to enhance reproducibility and experimentation.
It features modular components: a Redis-based storage for evolutionary units, an asyncio DAG execution engine, a MAP-Elites quality-diversity algorithm with single- and multi-island support, and a LangGraph-based mutation operator supporting rewrite and diff modes.
The framework uses Hydra for configuration and a directory-based problem specification.
Evaluations on benchmark problems show GigaEvo reproduced AlphaEvolve's Heilbronn triangle result (0.0364 vs 0.0365) and slightly surpassed circle packing results (2.63598 for n=26, 2.939 for n=32).
For kissing numbers in dimension 12, it recovered the known lower bound of 840 using Gemini-2.5-Flash but could not improve it.
On bin packing, GigaEvo matched FunSearch for uniform distribution and set a new state-of-the-art for Weibull (0.55% excess bins).
For prompt and agent evolution on a Reddit classification task, it improved AUC from 0.670 to 0.803.
Key insights include the robustness of rewrite-based mutation, benefits of bidirectional lineage tracking, and heterogeneous LLM routing.
The framework is available at https://github.com/AIRI-Institute/gigaevo-core.
02 From the paper
Abstract
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.