  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.
