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Author: search.filters.author.Matsumoto, Tetsuya

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    Redundancies in linear GP, canonical transformation, and its exploitation: A demonstration on image feature synthesis
    (2011-03-01)
    Watchareeruetai, Ukrit
    ;
    Takeuchi, Yoshinori
    ;
    Matsumoto, Tetsuya
    ;
    Kudo, Hiroaki
    ;
    Ohnishi, Noboru
    This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP. © 2010 Springer Science+Business Media, LLC.
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    Multi-objective genetic programming with redundancy-regulations for automatic construction of image feature extractors
    (2010-01-01)
    Watchareeruetai, Ukrit
    ;
    Matsumoto, Tetsuya
    ;
    Takeuchi, Yoshinori
    ;
    Kudo, Hiroaki
    ;
    Ohnishi, Noboru
    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-TT. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-TT and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.