Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs

The paper introduces MECo, a large language model–driven multi‑task evolutionary framework that enables zero‑shot cross‑problem generalization for combinatorial optimization. MECo maintains task‑conditioned heuristic populations, uses a transfer gap based on cross‑task performance to guide interactions, and selects a compact heuristic set that covers source combinations. Experiments on 32 variants of vehicle routing and flexible job‑shop scheduling demonstrate that MECo achieves lower mean costs than eight automated heuristic design baselines and improves both in‑domain and out‑of‑domain performance when integrated with other methods.

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• llms