An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem

摘要

The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. This paper applies a hybridized version of the Differential Evolution algorithm, the Island Memetic Differential Evolution algorithm, for solving the feature subset selection problem while the Nearest Neighbor Classification method is used for the classification task. The performance of the proposed algorithm is tested using various benchmark datasets from the UCI Machine Learning Repository. The algorithm is compared with variants of the differential evolution algorithm, a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm and with a number of algorithms from the literature.