Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

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@Article{Watchareeruetai:2010:ieiceTIS,
  author =       "Ukrit Watchareeruetai and Tetsuya Matsumoto and 
                 Yoshinori Takeuchi and Hiroaki Kudo and 
                 Noboru Ohnishi",
  title =        "Multi-Objective Genetic Programming with
                 Redundancy-Regulations for Automatic Construction of
                 Image Feature Extractors",
  journal =      "IEICE Transactions on Information and Systems",
  year =         "2010",
  number =       "9",
  volume =       "93-D",
  pages =        "2614--2625",
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective optimization, redundancy regulation,
                 image feature extraction, non-dominated sorting",
  ISSN =         "1745-1361",
  URL =          "http://search.ieice.org/bin/summary.php?id=e93-d_9_2614",
  bibdate =      "2010-12-04",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ieicet/ieicet93d.html#WatchareeruetaiMTKO10",
  abstract =     "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-II. 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-II
                 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.",
}

Genetic Programming entries for Ukrit WatchAreeruetai Tetsuya Matsumoto Yoshinori Takeuchi Hiroaki Kudo Noboru Ohnishi

Citations