Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs
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 enab...
| Authors | Zhuoliang Xie, Changliang Zhou, Genghui Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 8 October 2026 |
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