Comparison Study of Controlling Bloat Model of GP in Constructing Filter for Cell Image Segmentation Problems

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

@InProceedings{Yamaguchi:2012:CEC,
  title =        "Comparison Study of Controlling Bloat Model of {GP} in
                 Constructing Filter for Cell Image Segmentation
                 Problems",
  author =       "Hiroaki Yamaguchi and Tomoyuki Hiroyasu and 
                 Sakito Nunokawa and Noriko Koizumi and Naoki Okumura and 
                 Hisatake Yokouchi and Mitsunori Miki and 
                 Masato Yoshimi",
  pages =        "3503--3510",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6252995",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Applications
                 of Evolutionary Computation in Biomedical Engineering
                 (IEEE-CEC), Biometrics, bioinformatics and biomedical
                 applications",
  abstract =     "The final goal of this research is to construct a cell
                 image analysis system for supporting corneal
                 regenerative medicine. Existing image analysis software
                 requires knowledge about image processing of users
                 because users have to combine several image processing
                 on its analysis. Therefore, several types of methods to
                 construct the objective image processing automatically
                 using genetic programming (GP) have been proposed.
                 However, in conventional researches, only canonical GP
                 models were used. In this paper, GP models suited to
                 cell image segmentation are investigated applying
                 proposed controlling bloat model of GP. Applied models
                 were six types in addition to the canonical model;
                 those are Double Tournament, Tarpeian, Non-Destructive
                 Crossover (NDC), Recombinative Hill-Climbing (RHC),
                 Spatial Structure + Elitism (SS+E). The combination of
                 image processing obtained by these GP models and the
                 robustness are examined by comparative experiments,
                 using corned endothelium cell image. The experiment
                 results showed that SS+E is superior to other models in
                 both robustness and image processing constructed for
                 cell image segmentation, without depending on
                 parameters of tree depth limit and penalty.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Hiroaki Yamaguchi Tomoyuki Hiroyasu Sakito Nunokawa Noriko Koizumi Naoki Okumura Hisatake Yokouchi Mitsunori Miki Masato Yoshimi

Citations