Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis

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@Article{Watchareeruetai:2011:GPEM,
  author =       "Ukrit Watchareeruetai and Yoshinori Takeuchi and 
                 Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi",
  title =        "Redundancies in linear GP, canonical transformation,
                 and its exploitation: a demonstration on image feature
                 synthesis",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2011",
  volume =       "12",
  number =       "1",
  pages =        "49--77",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Linear
                 genetic programming, Redundant representation,
                 Canonical form, Canonical transformation, Feature
                 extraction",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-010-9118-x",
  abstract =     "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.",
  affiliation =  "Nagoya University Department of Media Science,
                 Graduate School of Information Science Furo-cho,
                 Chikusa-ku Nagoya 464-8603 Japan",
  notes =        "replaces \cite{Watchareeruetai:2008:cec}",
}

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

Citations