6 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
Xiaoming Xue, Yao Hu, Liang Feng +3
Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications. Despite many soph…
Design Principle Transfer in Neural Architecture Search via Large Language Models
Xun Zhou, Xingyu Wu, Liang Feng +2
Transferable neural architecture search (TNAS) has been introduced to design efficient neural architectures for multiple tasks, to enhance the practical applicability of NAS in rea…
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…
Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
Zeyi Wang, Songbai Liu, Jianyong Chen +1
Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid conve…
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Yuxiao Huang, Wenjie Zhang, Liang Feng +2
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitat…
Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking
Yinglan Feng, Liang Feng, Songbai Liu +2
Evolutionary Multitasking (EMT) paradigm, an emerging research topic in evolutionary computation, has been successfully applied in solving high-dimensional feature selection (FS) p…