@inproceedings{liu-etal-2026-experience,
    title = "Experience-Driven Reflective Co-Evolution of Prompts and Heuristics for Autonomous Algorithm Design",
    author = "Liu, Yihong  and
      Li, Junyi  and
      Lu, Hongyu  and
      Zhao, Xin  and
      Wen, Ji-Rong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.245/",
    pages = "4981--4995",
    ISBN = "979-8-89176-395-1",
    abstract = "Combinatorial optimization has long been dominated by manually engineered heuristics, a paradigm requiring substantial expert intuition and implementation overhead. The advent of Large Language Models has disrupted this landscape, enabling the autonomous synthesis and optimization of algorithms. Recent approaches typically iterate on heuristic populations using LLMs as mutators; however, these strategies often suffer from limited exploration, leading to stagnation in local optima. To overcome this, we present the Experience-Driven Reflective Co-\textbf{Evo}lution of \textbf{P}rompt and \textbf{H}euristics (\textbf{EvoPH}) for autonomous algorithm design, a novel framework that couples an island migration model with elite selection to maintain population diversity. Uniquely, EvoPH co-evolves both the guiding prompts and the heuristics themselves, using a feedback loop driven by past experience to refine the search process. We demonstrate EvoPH{'}s efficacy on the Traveling Salesman and Bin Packing Problems. Our results show that EvoPH achieves superior accuracy compared to baselines, marking a significant step forward in LLM-aided algorithm design."
}