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.
Automatic Heuristic Design (AHD) has emerged as a transformative approach for solving combinatorial optimization problems. While recent Large Language Model (LLM)-based methods have shown promise, they predominantly rely on fixed evolutionary operators and struggle to effectively accumulate and reuse historical search experience.
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?
arXiv:2608. 07544v1 Announce Type: cross Abstract: Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs).
arXiv:2604.04940v3 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise....
arXiv:2606. 00718v1 Announce Type: new Abstract: While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuristics as a single operator or search strategy, limiting their ability to model strong coupling among multiple decision substructures in problems such as the Traveling Thief Problem (TTP) and the Traveling Purchaser Problem (TPP).
arXiv:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.
arXiv:2608. 03636v1 Announce Type: cross Abstract: Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems.
arXiv:2608. 06808v1 Announce Type: new Abstract: The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems.
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
arXiv:2609.00023v1 Announce Type: cross Abstract: In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Au...
arXiv:2605. 08756v2 Announce Type: replace Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs).
The paper presents Iterative Sequential Transfer (IST), a method for few-shot multiobjective multitask optimization that addresses the bottleneck of aligning elite solution distributions across tasks. IST treats multitask optimization as a sequence of transfer problems, focusing evaluations on one target task per iteration and using a likelihood-informed prioritization to select the task most ready for knowledge integration. Experiments on benchmark and real-world problems demonstrate IST’s effectiveness under tight evaluation budgets.