11 citations · 26 across the 14 of their papers we have counts for
6 papers · 1 filter
Online Automated Algorithm Design with Large Language Models
Zhiyao Zhang, Yichen Li, Xingyu Wu +2
Large language models (LLMs) enable automated algorithm design (AAD) through reasoning and code synthesis. However, most existing LLM-based AAD methods separate algorithm design fr…
How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design
Yuhan Wang, Chaoda Peng, Xingyu Wu +2
Large language models (LLMs) have made automated heuristic design (AHD) increasingly practical by generating executable heuristic code from task descriptions and evaluator feedback…
Semantics-Aware Bilevel Co-Evolution: Towards Automated Multicomponent Algorithm Design
Zhiyao Zhang, Shenghao Wu, Xingyu Wu +1
LLM-assisted evolutionary search (LES) has emerged as a promising paradigm for automated algorithm design. However, existing methods usually suffer from two inherent limitations wh…
Learning to Transfer for Evolutionary Multitasking
Sheng-Hao Wu, Yuxiao Huang, Xingyu Wu +3
Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a s…
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Beichen Huang, Xingyu Wu, Yu Zhou +4
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diver…
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Xingyu Wu, Sheng-hao Wu, Jibin Wu +2
Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artif…