{
  "id": 17176437,
  "title": "HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization",
  "url": "https://urgent.news/2026/10/29/hemo-harness-self-evolution-for-multi-objective-combinatorial",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-29T13:32:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.40023v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}