Transformation of Redundant Representations of Linear Genetic Programming into Canonical Forms for Efficient Extraction of Image Features

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Watchareeruetai:2008:cec,
  author =       "Ukrit Watchareeruetai and Yoshinori Takeuchi and 
                 Tetsuya Matsumoto and Noboru Ohnishi",
  title =        "Transformation of Redundant Representations of Linear
                 Genetic Programming into Canonical Forms for Efficient
                 Extraction of Image Features",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "1996--2003",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0478.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631062",
  abstract =     "Recently, evolutionary computation (EC) has been
                 adopted to search for effective feature extraction
                 programs for given image recognition problems. For this
                 approach, feature extraction programs are constructed
                 from a set of primitive operations (POs), which are
                 usually general image processing and pattern
                 recognition operations. In this paper, we focus on an
                 approach based on a variation of linear genetic
                 programming (LGP). We describe the causes of
                 redundancies in LGP based representation, and propose a
                 transformation that converts the redundant LGP
                 representation into a canonical form, in which all
                 redundancies are removed. Based on this transformation,
                 we present a way to reduce computation time, i.e., the
                 evolutionary search that avoids executions of redundant
                 individuals. Experimental results demonstrate a success
                 in computation time reduction; around 7-62percent of
                 total compuation time can be reduced. Also, we have
                 experimented with an evolutionary search that prohibits
                 existence of redundant individuals. When selection
                 pressure is high enough, its search performance is
                 better than that of conventional evolutionary search.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

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

Citations