Two Improvements in Genetic Programming for Image Classification

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

@InProceedings{Li16:2008:cec,
  author =       "Yamin Li and Jinru Ma and Qiuxia Zhao",
  title =        "Two Improvements in Genetic Programming for Image
                 Classification",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "2492--2497",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0573.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631132",
  abstract =     "A new classification algorithm for multi-image
                 classification in genetic programming (GP) is
                 introduced, which is the centred dynamic class boundary
                 determination with quick-decreasing power value of
                 arithmetic progression. In the classifier learning
                 process using GP for multi-image classification,
                 different sets of power values are tested to achieve a
                 more suitable range of margin values for the
                 improvement of the accuracy of the classifiers. In the
                 second development, the program size is introduced into
                 the fitness function to control the size of program
                 growth during the evolutionary learning process. The
                 approach is examined on a Chinese character image data
                 set and a grass leaves data set, both of which have
                 four or more classes. The experimental results show
                 that while dealing with complicated problems of
                 multi-image classification, the new approach can be
                 used for more accurate classification and work better
                 than the previous algorithms of either static or
                 dynamic class boundary determination. With the fitness
                 function, the size of the programs in the population
                 can be controlled effectively and shortened
                 considerably during evolution. Thus, the readability of
                 the programs could be seemingly improved.",
  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 Ya-Min Li Jinru Ma Qiuxia Zhao

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