AlgoEvo：用于自动算法发现的自进化智能体搜索

中文摘要 AI 辅助

大语言模型通过合成可执行代码推进了自动算法发现，但现有框架将模型限制在具有预定义控制流的刚性搜索流程中。这种限制阻碍了自适应推理，阻断了跨范式迁移，并丢弃了宝贵的执行反馈。我们提出AlgoEvo，一个统一的智能体框架，将自动算法发现转变为交互式、知识积累的过程。自主智能体基于运行时反馈动态检查、诊断和编辑代码。设计技能中心将范式特定知识与核心发现引擎解耦，使单一工作流能够无缝处理单目标、多目标和多组件设计。同时，分层经验机制将搜索轨迹组织成任务级树以指导探索，并将跨任务模式整合为可复用技能。在六个代表性基准任务中，AlgoEvo以显著更少的评估次数和更低的令牌消耗匹配或超越专门方法，展示了强大的任务内积累、跨任务迁移能力，以及通过灵活技能激活复现或超越现有最先进性能的能力。

英文摘要

Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and overlooks richer execution feedback. To bridge this gap, we introduce an end-to-end framework, AlgoEvo, a unified agentic architecture that transforms automated algorithm discovery into an interactive, knowledge-accumulating process. An autonomous agent dynamically inspects, diagnoses, and edits code based on runtime feedback. A design skill hub decouples paradigm-specific knowledge from the core discovery engine, allowing a unified workflow to seamlessly handle single-heuristic, multi-objective, and multi-component design. Meanwhile, a hierarchical experience bank organizes search trajectories into a task-level tree to guide exploration and consolidates cross-task patterns into reusable skills. Across six representative benchmark tasks, AlgoEvo reaches state-of-the-art performance with as little as 7% of the evaluation budget and reduced token consumption, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed the strongest existing methods through flexible skill activation.

发表机构

• City University of Hong Kong（香港城市大学）
• A*STAR（新加坡科技研究局）

机构由 AI 辅助整理，请以论文原文为准。