In this chapter, a new hybrid binary version of the bat algorithm (BA) is suggested to solve feature selection problems. In particular, BA is integrated with an enhanced version of the differential evolution algorithm (DE). In the proposed algorithm, the BA with its capacity for echolocation to explore the feature space is combined with DE and its ability to converge to the best global solution in the search space. The general performance of the proposed algorithm is investigated by comparing it with the original optimizers and the other optimizers that have been used for feature selection in the literature. The proposed algorithm and various t optimizers are applied over datasets obtained from the UCI repository. The results prove the ability of the proposed algorithm to search the feature space for optimal feature combinations.