Affiliations: [a] Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, Canada | [b] Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
Correspondence:
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Corresponding author: M.A. Tawhid, Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, Canada. E-mail: [email protected].
Abstract: In this paper, a new hybrid binary version of Genetic algorithm (GA) and enhanced particle swarm optimization (PSO) algorithm is presented in order to solve feature selection (FS) problem. The proposed algorithm is called Hybrid Binary Genetic Enhanced PSO Algorithm (HBGEPSO). In the proposed HBGEPSO algorithm, the GA is combined with its capacity for exploration of the data through crossover and mutation and enhanced version of the PSO with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBGEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for FS in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBGEPSO algorithm to search the feature space for optimal feature combinations.
