arXiv 2604.240432026-04-27cs.AI

A2DEPT：通过进化程序树实现的大型语言模型驱动的自动算法设计

A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees

The Institute of Intelligent Computing, University of Electronic Science and Technology of China（电子科技大学智能计算学院）；College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；Academy of Military Medical Sciences（军事医学科学院）

Bin Chen, Shouliang Zhu, Beidan Liu, Yong Zhao, Tianle Pu, Huichun Li, Zhengqiu Zhu

备注：: Preprint

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AI总结：

本文提出A2DEPT，通过进化程序树实现自动算法设计，突破传统固定模板限制，提升算法表达能力，实验显示在标准和高约束基准上优于现有方法。

摘要：

设计组合优化问题（COPs）的启发式方法是一个基础但具有挑战性的任务，传统上需要大量的领域专业知识。最近，基于大型语言模型（LLM）的自动启发式设计（AHD）在自主生成启发式组件方面表现出潜力，且几乎不需要人工干预。然而，大多数现有的基于LLM的AHD方法强制使用固定的算法模板以确保可执行性，这将搜索限制在组件级调优，并限制了系统级算法的表达能力。为了使超越刚性模板的开放性求解器合成成为可能，我们提出了通过进化程序树进行自动算法设计（A2DEPT），将LLM视为系统级算法架构师。A2DEPT通过树结构的进化搜索进行广泛的程序空间探索，结合混合选择和分层算子，实现完整算法的迭代优化。为了使开放性生成变得实用，我们通过轻量级的程序维护循环强制可执行性，该循环执行反馈驱动的修复。在实验中，A2DEPT在标准和高度受限的基准上均优于代表性的基于LLM的基线方法。在标准基准上，它将平均归一化最优性差距减少了9.8%，相对于最强的竞争对手AHD基线。

英文摘要：

Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large Language Model (LLM)-based Automated Heuristic Design (AHD) has shown promise in autonomously generating heuristic components with minimal human intervention. However, most existing LLM-based AHD methods enforce fixed algorithmic templates to ensure executability, which confines the search to component-level tuning and limits system-level algorithmic expressiveness. To enable open-ended solver synthesis beyond rigid templates, we propose Automated Algorithm Design via Evolutionary Program Trees (A2DEPT), which treats LLMs as system-level algorithm architects. A2DEPT explores the vast program space via a tree-structured evolutionary search with hybrid selection and hierarchical operators, enabling iterative refinement of complete algorithms. To make open-ended generation practical, we enforce executability with a lightweight program-maintenance loop that performs feedback-driven repair. In experiments, A2DEPT consistently outperforms representative LLM-based baselines on both standard and highly constrained benchmarks. On the standard benchmarks, it reduces the mean normalized optimality gap by 9.8% relative to the strongest competing AHD baseline.

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