A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms

TL;DR

This paper explores the use of Kolmogorov-Arnold Networks as surrogate models in Surrogate-assisted Evolutionary Algorithms, demonstrating their effectiveness in reducing function evaluations and improving optimization efficiency.

Contribution

It introduces KANs as a novel surrogate model for SAEAs, showing their applicability and benefits in optimization tasks.

Findings

KANs reduce the number of expensive function evaluations

KANs improve the efficiency of SAEAs

Experimental results show competitive performance of KANs

Abstract

Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function can significantly reduce reliance on the function evaluations during the search process, thereby lowering the optimization costs. The construction of surrogate models is a critical component in SAEAs, with numerous machine learning algorithms playing a pivotal role in the model-building phase. This paper introduces Kolmogorov-Arnold Networks (KANs) as surrogate models within SAEAs, examining their application and effectiveness. We employ KANs for regression and classification tasks, focusing on the selection of promising solutions during the search process, which consequently reduces the number of expensive function evaluations. Experimental results indicate that KANs demonstrate commendable performance within SAEAs, effectively decreasing the number of function calls and enhancing the optimization efficiency. The relevant code is publicly accessible and can be found in the GitHub repository.