15 papers
Generalizable Heuristic Generation Through LLMs with Meta-Optimization
Yiding Shi, Jianan Zhou, Wen Song +4
Heuristic design with large language models (LLMs) has emerged as a promising approach for tackling combinatorial optimization problems (COPs). However, existing approaches often r…
READY: Reward Discovery for Meta-Black-Box Optimization
Zechuan Huang, Zhiguang Cao, Hongshu Guo +2
Meta-Black-Box Optimization (MetaBBO) is an emerging avenue within Optimization community, where algorithm design policy could be meta-learned by reinforcement learning to enhance…
Evolutionary System 2 Reasoning: An Empirical Proof
Zeyuan Ma, Wenqi Huang, Guo-Huan Song +4
Machine intelligence marks the ultimate dream of making machines' intelligence comparable to human beings. While recent progress in Large Language Models (LLMs) show substantial sp…
Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering
Chen Wang, Yue-Jiao Gong, Zhiguang Cao +1
To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-le…
Probing Neural Combinatorial Optimization Models
Zhiqin Zhang, Yining Ma, Zhiguang Cao +1
Neural combinatorial optimization (NCO) has achieved remarkable performance, yet its learned model representations and decision rationale remain a black box. This impedes both acad…
SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy
Yong Liang Goh, Zhiguang Cao, Yining Ma +3
Recent advances toward foundation models for routing problems have shown great potential of a unified deep model for various VRP variants. However, they overlook the complex real-w…