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

arXiv: https://arxiv.org/abs/2405.16494 · HTML: https://mlsanity.com/paper/2405.16494/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2024.

## Built on by (most cited first, 7 of 7)

- [FC-KAN: Function Combinations in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2409.01763/) (2024)
- [Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study](https://mlsanity.com/paper/2503.18060/) (2025)
- [KAN v.s. MLP for Offline Reinforcement Learning](https://mlsanity.com/paper/2409.09653/) (2024)
- [AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning](https://mlsanity.com/paper/2503.06112/) (2025)
- [Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice](https://mlsanity.com/paper/2408.10263/) (2024)
- [HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation](https://mlsanity.com/paper/2501.18199/) (2025)
- [Combinations of Fast Activation and Trigonometric Functions in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2508.11876/) (2025)

## Abstract (arXiv)

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.