Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel

parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature selection is another factor that impacts

classifi cation accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM

classifi cation accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.