Abstract
Although Large Language Models have advanced Automated Heuristic Design, treating algorithm evolution as a monolithic text generation task overlooks the coupling between discrete algorithmic structures and continuous numerical parameters. Consequently, existing methods often discard promising algorithms due to uncalibrated constants and suffer from premature convergence resulting from simple similarity metrics. To address these limitations, we propose TIDE, a Tuning-Integrated Dynamic Evolution framework designed to decouple structural reasoning from parameter optimization. TIDE features a nested architecture where an outer parallel island model utilizes Tree Similarity Edit Distance to drive structural diversity, while an inner loop integrates LLM-based logic generation with a differential mutation operator for parameter tuning. Additionally, a UCB-based scheduler dynamically prioritizes high-yield prompt strategies to optimize resource allocation. Extensive experiments across nine combinatorial optimization problems demonstrate that TIDE discovers heuristics that significantly outperform state-of-the-art baselines in solution quality while achieving improved search efficiency and reduced computational costs.
Abstract (translated)
尽管大型语言模型已经推进了自动启发式设计，但将算法演化视为单一的文本生成任务忽视了离散算法结构与连续数值参数之间的耦合。因此，现有方法常常因未调校的常数而舍弃有前景的算法，并且由于简单的相似性度量而导致过早收敛。为了克服这些限制，我们提出了TIDE框架（一种集成调优的动态演化框架），旨在将结构推理与参数优化分离。TIDE具有一个嵌套架构，在该架构中，外部并行岛屿模型利用树相似编辑距离来驱动结构多样性，而内部循环则整合基于大型语言模型的逻辑生成与差分变异算子进行参数调整。此外，一种基于UCB（Upper Confidence Bound）调度器动态优先处理高收益提示策略以优化资源分配。 通过九个组合优化问题上的广泛实验表明，TIDE能够发现显著优于现有最佳基线算法的质量更佳的启发式方法，并且实现了改进的搜索效率和降低的计算成本。
URL
https://arxiv.org/abs/2601.21239
