关键词: 克隆选择,差分进化,支持向量机,核参数

Abstract: The parameters of support vector machine (SVM) are important factors affecting its performance.However,the absence of a mature theory about the kernel parameter selection of SVM heavily affects its wide application.This paper introduced clonal selection algorithm into differential evolution algorithm,and improved the strategies of basic clonal selection algorithm and differential evolution algorithm.Through combining the two algorithms mentioned above,a differential evolution algorithm based on clonal selection was proposed and applied to optimize the parameters of SVM kernel.The test results show that the algorithm can not only effectively avoid the premature-convergence problem of differential evolution algorithm,but also significantly improve the optimization ability.UCI wine database application data show that the algorithm can accelerate the parameter search speed,and improve the prediction accuracy and genera-lization ability of SVM.The high accuracy of classification and better generalization performance prove that using clonalselection differential evolution algorithm is a good way to optimize SVM kernel parameter.

Key words: Clone selection,Differential evolution,SVM,Kernel parameter