Researchers propose modular LLM framework for automated heuristic design
New structured approach decomposes heuristic discovery into three stages, achieving up to 0.17 QYI improvement.
Researchers Mingxin Yu, Ruixiao Yang, and Chuchu Fan propose a structured framework for LLM-driven Automated Heuristic Design (AHD) in combinatorial optimization. Their method explicitly decomposes the discovery process into three modular stages: forward pass evaluation, backward pass feedback, and program refinement. Validated across four real-world domains, it outperforms baselines and shows existing AHD methods are restricted versions of this framework, enabling systematic component upgrades.
Why It Matters
Provides a principled, modular approach to automate complex optimization tasks, reducing reliance on manual trial-and-error and domain expertise.