6 citations · 14 across the 11 of their papers we have counts for
11 papers
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…
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…
Fine-Grained Model Merging via Modular Expert Recombination
Haiyun Qiu, Xingyu Wu, Liang Feng +1
Model merging constructs versatile models by integrating task-specific models without requiring labeled data or expensive joint retraining. Although recent methods improve adaptabi…
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
Xiaoming Xue, Liang Feng, Yinglan Feng +3
Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in o…
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…
Multi-Space Evolutionary Search for Large-Scale Optimization
Liang Feng, Qingxia Shang, Yaqing Hou +2
In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify…