Choosing multiple parameters for SVM based on genetic algorithm

In recent years, research into SVMs has focused on two main areas. One is to improve the precision of the SVM algorithm, and another is to improve its speed. We propose a new method which can appropriately tune multiple parameters in the kernel functions of an SVM. It not only can improve the algorithm performance and make it approach to the real problem, but also can avoid those methods available which are too complex; the kernel must be differential and the result may be not optimal.