标题： 基于遗传算法的Kolmogorov-Arnold网络在分类任务中的自动优化方法 显示英文标题

标题： A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks

摘要： 为了解决多层感知器（MLPs）中的可解释性问题，2024年引入了柯尔莫戈罗夫-阿诺尔德网络（KANs）。然而，优化KAN结构是劳动密集型的，通常需要手动干预和参数调整。本文提出了GA-KAN，一种基于遗传算法的方法，能够自动优化KANs，在设计过程中无需人工干预。据我们所知，这是首次探索进化计算来自动优化KANs。此外，受MLPs中稀疏连接有效减少参数数量的启发，GA-KAN进一步探索了稀疏连接，以解决KANs中参数空间庞大的挑战。GA-KAN在两个玩具数据集上进行了验证，实现了最佳结果，而无需原始KAN所需的手动调优。此外，GA-KAN在五个分类数据集上表现出色，在所有数据集上均优于传统方法，并为Wine和Iris数据集提供了可解释的符号公式，从而增强了模型的透明度。此外，GA-KAN在所有五个数据集上显著减少了参数数量。GA-KAN的核心贡献包括自动化优化、一种新的编码策略和一种新的解码过程，这些共同提高了准确性与可解释性，并减少了参数数量。 显示英文摘要

摘要： To address the issue of interpretability in multilayer perceptrons (MLPs), Kolmogorov-Arnold Networks (KANs) are introduced in 2024. However, optimizing KAN structures is labor-intensive, typically requiring manual intervention and parameter tuning. This paper proposes GA-KAN, a genetic algorithm-based approach that automates the optimization of KANs, requiring no human intervention in the design process. To the best of our knowledge, this is the first time that evolutionary computation is explored to optimize KANs automatically. Furthermore, inspired by the use of sparse connectivity in MLPs in effectively reducing the number of parameters, GA-KAN further explores sparse connectivity to tackle the challenge of extensive parameter spaces in KANs. GA-KAN is validated on two toy datasets, achieving optimal results without the manual tuning required by the original KAN. Additionally, GA-KAN demonstrates superior performance across five classification datasets, outperforming traditional methods on all datasets and providing interpretable symbolic formulae for the Wine and Iris datasets, thereby enhancing model transparency. Furthermore, GA-KAN significantly reduces the number of parameters over the standard KAN across all the five datasets. The core contributions of GA-KAN include automated optimization, a new encoding strategy, and a new decoding process, which together improve the accuracy and interpretability, and reduce the number of parameters.