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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Watchareeruetai:2009:SMC,
  author =       "Ukrit Watchareeruetai and Tetsuya Matsumoto and 
                 Yoshinori Takeuchi and Hiroaki Kudo and 
                 Noboru Ohnishi",
  title =        "Construction of image feature extractors based on
                 multi-objective genetic programming with redundancy
                 regulations",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, 2009. SMC 2009",
  year =         "2009",
  pages =        "1328--1333",
  address =      "Texas, USA",
  month =        oct # " 11-14",
  keywords =     "genetic algorithms, genetic programming, feature
                 extraction, linear programming, sorting, MOGP-based
                 FEPs construction system, NSGA-II, feature extraction
                 programs, image feature extractors, linear genetic
                 programming, multiobjective genetic programming,
                 nondominated sorting evolutionary algorithm, population
                 diversity, program representation Multi-objective
                 optimization, image feature extraction, non-dominated
                 sorting, redundancy regulation",
  isbn13 =       "978-1-4244-2793-2",
  DOI =          "doi:10.1109/ICSMC.2009.5346242",
  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.",
  notes =        "Dept. of Media Sci., Nagoya Univ., Nagoya, Japan; Also
                 known as \cite{5346242}",
}

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

Citations