分类任务中Kolmogorov-Arnold Networks自动化优化的遗传算法方法
A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks
AI总结：
本文提出GA-KAN，一种基于遗传算法的KANs自动化优化方法，通过引入稀疏连接和新的编解码策略，在多个分类数据集上提升了模型准确性与可解释性，同时显著减少了参数数量。
摘要：
为了解决多层感知机（MLPs）的可解释性问题，Kolmogorov-Arnold Networks（KANs）于2024年被提出。然而，优化KAN结构是劳动密集型的，通常需要人工干预和参数调优。本文提出GA-KAN，一种基于遗传算法的方法，自动化优化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进一步探索稀疏连接以应对KANs中庞大的参数空间挑战。GA-KAN在两个玩具数据集上得到验证，无需原始KAN所需的人工调优即可获得最优结果。此外，GA-KAN在五个分类数据集上表现出卓越性能，在所有数据集上超越传统方法，并为Wine和Iris数据集提供可解释的符号公式，从而增强模型透明度。进一步地，GA-KAN在所有五个数据集上显著减少了标准KAN的参数数量。GA-KAN的核心贡献包括自动化优化、新的编码策略和新的解码过程，这些共同提高了准确性和可解释性，并减少了参数数量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
