An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems

Young-Seon Jeong, Kyoung Seok Shin and Myong K Jeong
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Young-Seon Jeong: Department of Industrial Engineering, Chonnam National University, Gwangju, Republic of Korea
Kyoung Seok Shin: Department of Industrial Engineering, Chonnam National University, Gwangju, Republic of Korea
Myong K Jeong: 1] Rutgers Center for Operations Research (RUTCOR), Rutgers University, Piscataway, NJ, USA[2] Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA

Journal of the Operational Research Society, 2015, vol. 66, issue 4, 529-538

Abstract: Several meta-heuristic algorithms, such as evolutionary algorithms (EAs) and genetic algorithms (GAs), have been developed for solving feature selection problems due to their efficiency for searching feature subset spaces in feature selection problems. Recently, hybrid GAs have been proposed to improve the performance of conventional GAs by embedding a local search operation, or sequential forward floating search mutation, into the GA. Existing hybrid algorithms may damage individuals’ genetic information obtained from genetic operations during the local improvement procedure because of a sequential process of the mutation operation and the local improvement operation. Another issue with a local search operation used in the existing hybrid algorithms is its inappropriateness for large-scale problems. Therefore, we propose a novel approach for solving large-sized feature selection problems, namely, an EA with a partial sequential forward floating search mutation (EAwPS). The proposed approach integrates a local search technique, that is, the partial sequential forward floating search mutation into an EA method. Two algorithms, EAwPS-binary representation (EAwPS-BR) for medium-sized problems and EAwPS-integer representation (EAwPS-IR) for large-sized problems, have been developed. The adaptation of a local improvement method into the EA speeds up the search and directs the search into promising solution areas. We compare the performance of the proposed algorithms with other popular meta-heuristic algorithms using the medium- and large-sized data sets. Experimental results demonstrate that the proposed EAwPS extracts better features within reasonable computational times.

Date: 2015
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