Feature selection by extracting the most informative features from dataset, improves the accuracy of a classifier, reduces its complexity and helps to speed up the classification tasks. Many meta-heurestic search beased algorithms have been proposed for feature selection challenge. In this paper a new hybrid feature selection method based on the combination of genetic algorithm (GA) and particle swarm optimization (PSO) is introduced. In this structure, instead of point by point search used in most of the search methods, the subspaces are sequentially determined by an enhanced genetic algorithm, where each subspace is efficiently searched by a PSO method. In the proposed genetic algorithm, each chromosome is equal to a subspace of the search space. Crossover and mutation operators over the defined chromosomes generate new subspaces. PSO algorithm searches in the zone, returns the fitness value of the corresponding chromosome. The idea of defining the subspaces is very efficient in utilizing exploration ability of GA. In addition, PSO exploitation reduces the time complexity of pure genetic search. Reported results on 10 UCI benchmark datasets confirm how this method has significant improvement in classification performance.
