SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution

2026-04-27

AI Summary

This work addresses key limitations in existing large language model (LLM)-based evolutionary search, which struggles to distinguish semantically equivalent yet syntactically diverse programs and fails to effectively preserve strategic potential or detect saturation within strategy families. To overcome these challenges, the authors propose introducing a strategy-space layer into program evolution, elevating natural language strategy descriptions to first-class evolutionary states. By integrating strategy representation, hierarchical experience retrieval, and strategy-landscape navigation, the method enables explicit organization, memory, and exploration at the strategy level. Combining LLMs with evolutionary algorithms, strategy clustering, behavior-complementarity-based retrieval, and summarization mechanisms, the approach significantly outperforms baseline methods across tasks in mathematical algorithm discovery, system optimization, and agent framework design, achieving a relative improvement of 21% in open-ended system optimization.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

LLM-guided evolutionary search has emerged as a promising paradigm for automated algorithm discovery, yet most systems track search progress primarily through executable programs and scalar fitness. Even when natural-language reflection is used, it is often used locally in mutation prompts or stored without an explicit population-level organization of strategic directions. As a result, evolutionary search can struggle to distinguish syntactically different implementations of the same idea, preserve lower-fitness but strategically promising directions, or detect when an entire family of strategies has saturated. We introduce \model, a modular strategy-space layer that elevates natural-language strategy descriptions from transient prompt context to first-class population-level evolutionary state in LLM-driven program search. \model augments each candidate program with an explicit natural language strategy description and uses this representation in three ways: Strategy Articulation turns mutation into a diagnose-direct-implement process; Stratified Experience Retrieval organizes the archive into strategy clusters and selects inspirations by behavioral complementarity; and Strategic Landscape Navigation periodically summarizes effective, saturated, and underexplored strategy families to guide future mutations. Across mathematical algorithm discovery, systems optimization, and agent-scaffold benchmarks, \model improves the underlying evolutionary backbones in most settings, with particularly large gains (21% relative improvement) on open-ended system optimization tasks. These results suggest that persistent strategy representations provide a practical mechanism for improving the robustness and efficiency of LLM-guided evolutionary search, suggesting a path toward compound AI systems that accumulate algorithmic knowledge over time.

Problem

• algorithm discovery
• evolutionary search
• strategy representation
• LLM-guided optimization
• program synthesis

Innovation

• strategy-space evolution
• LLM-guided search
• algorithm discovery
• natural-language strategy representation
• evolutionary computation