Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data mining performance gets reduced by irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives are maximizing the classification performance and minimizing the number of features. Moreover, the existing feature selection algorithms treat the task as a single objective problem.