HeurEvo：面向时间紧迫数学优化的混合求解器增强启发式算法的智能体进化

中文摘要 AI 辅助

近期在智能体启发式设计方面的进展利用AI智能体和执行反馈来自动化发现针对具有挑战性的优化问题的算法。在许多实际场景中，必须在严格的运行时约束下获得高质量的解决方案，这促使了将问题特定启发式算法与强大的数学规划求解器相结合的混合方法。然而，现有方法通常是在预定义流程内改进启发式组件，或孤立地调整求解器配置。这限制了对计算分配位置、如何利用求解器以及如何改进整体算法结构的整体适应。为解决这些限制，我们提出了HeurEvo，一个自动化的计划-代码-组件协同进化框架，它联合进化高层算法结构、其实现以及一个共享的可重用组件池。一个规划器决定使用哪些算法组件、如何组合它们以及如何在各个阶段分配运行时，一个编码器将生成的计划实现为可执行代码，而一个组件进化器更新共享组件池。在基于岛屿的进化框架内，计划和实现与一个解释器智能体的反馈共同进化，该智能体分析执行结果并识别改进机会。在多种组合优化基准和具有挑战性的MIPLIB实例上，HeurEvo在严格的运行时预算内找到高质量解决方案，通常能匹配或超越在数小时或数天计算下最先进的优化求解器。在几个非线性几何问题（如六边形堆积）上，它也改进了先前报告的最佳结果。这些结果凸显了在智能体启发式设计中联合搜索算法结构和实现的价值。

英文摘要

Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings, high-quality solutions must be obtained under strict runtime constraints, motivating hybrid approaches that combine problem-specific heuristics with powerful mathematical programming solvers. However, existing approaches typically improve heuristic components within predefined procedures or tune solver configurations in isolation. This limits holistic adaptation of where to allocate computation, how to leverage solvers, and how to refine the overall algorithmic structure. To address these limitations, we propose HeurEvo, an automated plan--code--component co-evolution framework that jointly evolves the high-level algorithmic structures, their implementations, and a shared pool of reusable components. A planner determines which algorithmic components to use, how to combine them, and how to allocate runtime across stages, a coder realizes the resulting plan as executable code, while a component evolver updates the shared component pool. Within an island-based evolutionary framework, plans and implementations co-evolve with feedback from an interpreter agent that analyzes execution results and identifies opportunities for improvement. Across diverse combinatorial optimization benchmarks and challenging MIPLIB instances, HeurEvo finds high-quality solutions within tight runtime budgets, often matching or surpassing state-of-the-art optimization solvers given hours or days of computation. On several nonlinear geometry problems such as hexagon packing, it also improves upon the best previously reported results. These results highlight the value of jointly searching over algorithmic structure and implementation for agentic heuristic design.

发表机构

• Microsoft（微软）
• Purdue University（普渡大学）

机构由 AI 辅助整理，请以论文原文为准。