English(EN) HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization

HEMO系统赋能组合优化中的自进化算子

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-29 13:32

研究人员开发了HEMO，这是一种用于多目标组合优化（MOCO）的新型自动算子设计（AOD）方法。与仅进化搜索算子的先前方法不同，HEMO使AOD本身能够自我进化。该系统在解决方案质量和运行时间的指导下，持续更新其检索经验、改进算子、选择父代和编排搜索步骤的逻辑。HEMO利用持久化内存文件系统，在连续状态中构建累积的优化经验，在各种MOCO任务上展示了强大的性能和可迁移性。 AI

影响这项研究引入了一种新颖的自进化方法来优化复杂问题，有可能提高依赖组合优化的领域的效率。

排序理由这是一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Zhiguang Cao · 2026-10-29 13:32

HEMO：利用自演化实现多目标组合优化

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 …

本文如何被排名

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HEMO：利用自演化实现多目标组合优化

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