arXiv：cs.AI· Zhouliang Xie, Changliang Zhou, Genghui Li, Zhenkun Wang·· 5 小时前AI 评分38
MECo：基于 LLM 的多任务进化框架实现零样本跨问题泛化
Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs
AI 导读
研究者提出 LLM 驱动的多任务进化框架 MECo，通过任务条件化启发式种群与基于跨任务种群表现的迁移差距引导启发式迁移重组，实现零样本跨问题泛化。在车辆路径（VRP）与柔性作业车间调度（FJSP）的 32 个问题变体上，MECo 在相同预算下平均成本低于 8 个自动启发式设计（AHD）基线，并在域外问题上优于各家族最强基线。将该框架与不同 AHD 方法结合还能提升其 ID 与 OOD 表现。
正文
Abstract:Designing effective heuristics for diverse combinatorial optimization problems requires substantial expertise and repeated search. Large language models (LLMs) automate heuristic generation and refinement, but heuristic search typically depends on evaluation feedback from the problem being optimized. Generalizing to new problem definitions using only source-task feedback therefore remains a central challenge. We introduce MECo, an LLM-driven multi-task evolutionary framework for zero-shot cross-problem generalization. MECo maintains task-conditioned heuristic populations and uses a transfer gap based on cross-task population performance to guide their interactions. These interactions enable the transfer and recombination of heuristics. A complementary selection criterion then constructs a compact heuristic set by rewarding each member's additional coverage of source combinations. The selected set is applied to target problems without further search or adaptation. Experiments on 32 problem variants across vehicle routing (VRP) and flexible job-shop scheduling (FJSP) show that MECo achieves the lowest mean costs compared with eight automated heuristic design (AHD) baselines under the same budgets. On out-of-domain problems, it outperforms the strongest baseline in each family. Moreover, integrating the framework of MECo with different AHD methods improves their ID and OOD performance in both families, supporting its effectiveness across different methods.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.03316 [cs.AI] |
| (or arXiv:2610.03316v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03316 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xie Zhuoliang [view email]
[v1]
Fri, 2 Oct 2026 13:53:34 UTC (338 KB)
来源：arXiv：cs.AI · arxiv.org