7 citations · 19 across the 12 of their papers we have counts for
12 papers
Relation Reasoning with LLMs in Expensive Optimization
Ye Lu, Bingdong Li, Aimin Zhou +1
Expensive optimization problems (EOPs) are black-box tasks with costly objective evaluations and no gradient access, making the evaluation budget the key bottleneck. Surrogate-assi…
IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization
Shuai Wang, Yaoming Yang, Bingdong Li +2
Learning Path Recommendation (LPR) aims to generate personalized sequences of learning items that maximize long-term learning effect while respecting pedagogical principles and ope…
EA4LLM: A Gradient-Free Approach to Large Language Model Optimization via Evolutionary Algorithms
WenTao Liu, Siyu Song, Hao Hao +1
In recent years, large language models (LLMs) have made remarkable progress, with model optimization primarily relying on gradient-based optimizers such as Adam. However, these gra…
A Large Language Model for Chemistry and Retrosynthesis Predictions
Yueqing Zhang, Wentao Liu, Yan Zhang +9
Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce doma…
Un-evaluated Solutions May Be Valuable in Expensive Optimization
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study…
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
Bingdong Li, Zixiang Di, Yanting Yang +5
In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algo…
Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Large Language Models (LLMs) have achieved significant progress across various fields and have exhibited strong potential in evolutionary computation, such as generating new soluti…
A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
Hao Hao, Xiaoqun Zhang, Bingdong Li +1
Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function…
Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This…
Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving…
Evolutionary Retrosynthetic Route Planning
Yan Zhang, Hao Hao, Xiao He +2
Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also a…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…