HEMO：让 LLM 自动算子设计的搜索框架自身进化
HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization
论文把进化搜索的框架本身也变成可进化对象，用 UCB 调度器决定改哪个模块，在 8 个多目标优化任务上跑赢了现有基线，做组合优化的可以看看。
arXiv 论文提出 HEMO，针对多目标组合优化（MOCO）的自动算子设计（AOD）问题，把框架本身作为进化目标。HEMO 通过折扣 UCB 调度器逐模块更新经验检索、父代选择、算子改进和搜索编排逻辑，并利用持久化记忆文件系统跨框架状态保存结果与诊断反馈。在 8 个 MOCO 任务和 2 种进化骨干上的实验显示，HEMO 优于传统进化、神经方法和基于 LLM 的 AOD 基线，并能迁移到相关任务。
HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization
Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-specific search and variation operators for multi-objective combinatorial optimization (MOCO). Yet most methods evolve the operators while leaving the process that discovers them fixed. This limits adaptation as new Pareto trade-offs and operator interactions change where and how the search should proceed. We introduce HEMO, which makes the AOD harness itself a self-evolving optimization target. HEMO maintains a persistent harness state and updates one module at a time, revising the logic that determines which experience to retrieve, which operators to improve, which parents to select, and how to formulate the orchestration of search steps. Downstream solution quality and runtime guide these updates, while a discounted UCB scheduler directs adaptation toward promising modules as their utility changes. A persistent memory filesystem preserves candidate outcomes and diagnostic feedback across harness states, enabling successive states to build on accumulated optimization experience. Experiments across eight MOCO tasks and two structurally distinct evolutionary backbones demonstrate strong performance against conventional evolutionary, neural, and LLM-based AOD baselines, together with effective transfer to related tasks. HEMO extends automatic operator design beyond evolving individual operators to improving the search process that discovers them.