用经验反馈引导提示与启发式算法共同进化，提升自动算法设计效果
Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design
- 将提示词与启发式算法一起演化，通过性能反馈持续优化
- 在旅行商和装箱问题上相对最优解误差最低
- 适合想自动化设计优化算法的研究者参考
组合优化问题传统上依赖人工设计的启发式算法，需大量领域知识和实现成本。近期研究显示，大语言模型（LLMs）可驱动自动启发式算法设计，实现启发式的自动生成与迭代优化。这类方法通常维护一个启发式种群，并使用LLM作为变异算子进行多代演化。然而，易陷入局部最优。为此，本文提出经验引导的提示与启发式协同进化框架（EvoPH），融合岛屿迁移模型与精英选择算法，模拟多样化的启发式种群。在EvoPH中，提示词与启发式算法共同演化，由性能反馈指导。我们在旅行商问题（TSP）和装箱问题（BPP）上评估该框架，实验表明，EvoPH在两个数据集上均达到最低相对误差，推动了基于LLM的自动算法设计发展。
原文摘要 · Abstract (English)
Combinatorial optimization problems are traditionally tackled with handcrafted heuristic algorithms, which demand extensive domain expertise and significant implementation effort. Recent progress has highlighted the potential of automatic heuristics design powered by large language models (LLMs), enabling the automatic generation and refinement of heuristics. These approaches typically maintain a population of heuristics and employ LLMs as mutation operators to evolve them across generations. While effective, such methods often risk stagnating in local optima. To address this issue, we propose the Experience-Guided Reflective Co-Evolution of Prompt and Heuristics (EvoPH) for automatic algorithm design, a novel framework that integrates the island migration model with the elites selection algorithm to simulate diverse heuristics populations. In EvoPH, prompts are co-evolved with heuristic algorithms, guided by performance feedback. We evaluate our framework on two problems, i.e., Traveling Salesman Problem and Bin Packing Problem. Experimental results demonstrate that EvoPH achieves the lowest relative error against optimal solutions across both datasets, advancing the field of automatic algorithm design with LLMs.
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