7 citations · 19 across the 12 of their papers we have counts for
5 papers · 1 filter
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…