I. Introduction
In recent years, the Support Vector Machine (SVM) has gained recognition as a potent tool in machine learning for classification and regression tasks [1]. However, challenges arise in determining optimal parameter values, such as the penalty parameter and the choice of the kernel function, leading to suboptimal model performance and potential drawbacks.