Combinatorial optimization problems often rely on heuristic algorithms to generate efficient solutions. However, the manual design of heuristics is resource-intensive and constrained by the designer's expertise. Recent advances in artificial intelligence, particularly large language models (LLMs), have demonstrated the potential to automate heuristic generation through evolutionary frameworks. Recent works focus only on well-known combinatorial optimization problems like the traveling salesman problem and online bin packing problem when designing constructive heuristics. This study investigates whether LLMs can effectively generate heuristics for niche, not yet broadly researched optimization problems, using the unit-load pre-marshalling problem as an example case. We propose the Contextual Evolution of Heuristics (CEoH) framework, an extension of the Evolution of Heuristics (EoH) framework, which incorporates problem-specific descriptions to enhance in-context learning during heuristic generation. Through computational experiments, we evaluate CEoH and EoH and compare the results. Results indicate that CEoH enables smaller LLMs to generate high-quality heuristics more consistently and even outperform larger models. Larger models demonstrate robust performance with or without contextualized prompts. The generated heuristics exhibit scalability to diverse instance configurations.
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Peking University（北京大学）；Southeast University（东南大学）；Singapore Management University（新加坡管理大学）；KAIST（韩国科学技术院）；PKU-Wuhan Institute for Artificial Intelligence（北京大学武汉人工智能研究院）
Leveraging Large Language Models to Build and Execute Computational Workflows
Universidad San Francisco de Quito（基多圣弗朗西斯科大学）；University of Missouri（密苏里大学）；National Center for Supercomputing Applications（美国国家超级计算应用中心）；University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
Boosting Universal LLM Reward Design through Heuristic Reward Observation Space Evolution
Center on Frontiers of Computing Studies, School of Computer Science, Peking University（北京大学计算机学院计算前沿研究中心）；PKU-Agibot Lab, School of Computer Science, Peking University（北京大学计算机学院PKU-Agibot实验室）；National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；School of Automation, Southeast University（东南大学自动化学院）
Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models
Sun Yat-Sen University（中山大学）；Zhejiang University（浙江大学）；BraneMatrix AI；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Renmin University of China（中国人民大学）
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA（弗吉尼亚理工大学工业与系统工程系）；Department of Computer Science, Virginia Tech, Arlington, VA（弗吉尼亚理工大学计算机科学系）
Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies
Management \& Technology University of Pennsylvania Philadelphia, PA, USA；Systems Engineering Institute Technology of Sepuluh Nopember (ITS) Surabaya, Indonesia；Systems Engineering King Fahd University of Petroleum
