Construction of image feature extractors based on multi-objective genetic programming with redundancy regulations

Authors:
Ukrit Watchareeruetai;Tetsuya Matsumoto;Yoshinori Takeuchi;Hiroaki Kudo;Noboru Ohnishi
Affiliations:
Department of Media Science, Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Department of Media Science, Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Department of Media Science, Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Department of Media Science, Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Department of Media Science, Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity. Experimental results exhibit that the proposed MOGP-based FEPs construction system provides obviously better performance than the original non-dominated sorting approach.