中文摘要：

特征子集选择和训练参数的优化一直是SVM研究中的两个重要方面，选择合适的特征和合理的训练参数可以提高SVM分类器的性能，以往的研究是将两个问题分别进行解决。随着遗传优化等自然计算技术在人工智能领域的应用，开始出现特征选择及参数的同时优化研究。研究采用免疫遗传算法（IGA）对特征选择及SVM参数的同时优化，提出了一种IGA—SVM算法。实验表明，该方法可找出合适的特征子集及SVM参数，并取得较好的分类效果，证明算法的有效性。

英文摘要：

Feature selection and parameter optimization are two important aspects for improving classifier performance.However, they are solved separately traditionally.Recently,with the wide applications of evolutionary computation in pattern recognition area, simultaneous feature selection and parameter optimization become possible and tendency.To solve this problem,a simultaneous feature selection and SVM parameter optimization algorithm based on Immune GA algorithm is proposed,named as IGA-SVM.The experimental results show that the algorithm can efficiently find the suitable feature subsets and SVM parameters,which result in a good classification performance.