5 papers
ES-AHD: An Evolution Strategy Framework for Automatic Heuristic Design
Yutao Lai, Kezhao Lai, Hai-Lin Liu +2
In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Automatic Heuristic Design (AHD).…
Bi-EZP: LLM-Guided Bilevel Program Evolution for Ensemble Zero-Cost Proxy Discovery
Yutao Lai, Kezhao Lai, Hai-Lin Liu
Zero-cost proxies enable neural architecture search (NAS) to rank candidate networks from statistics computed at initialization, avoiding repeated training. However, different prox…
SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems
Kezhao Lai, Yutao Lai, Hai-Lin Liu
LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components. In large-scale routing problems, localized…
CoAction: Cross-task Correlation-aware Pareto Set Learning
Xinyue Chen, Yingxuan Liang, Yiqin Huang +3
Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions. However, existi…
Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design
Kezhao Lai, Yutao Lai, Hai-Lin Liu
While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical over-exploitation tendency under…