A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification

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

@InCollection{Zhang:2009:EIASP,
  author =       "Mengjie Zhang and Mark Johnston",
  title =        "A Variant Program Structure in Tree-Based Genetic
                 Programming for Multiclass Object Classification",
  booktitle =    "Evolutionary Image Analysis and Signal Processing",
  publisher =    "Springer",
  year =         "2009",
  editor =       "Stefano Cagnoni",
  volume =       "213",
  series =       "Studies in Computational Intelligence",
  pages =        "55--72",
  address =      "Berlin / Heidelberg",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01635-6",
  ISSN =         "1860-949X",
  DOI =          "doi:10.1007/978-3-642-01636-3_4",
  abstract =     "This chapter describes an approach to the use of
                 genetic programming for multiclass object
                 classification. Instead of using the standard
                 tree-based genetic programming approach, where each
                 genetic program returns just one floating point number
                 that is then translated into different class labels,
                 this approach invents a new program structure with
                 multiple outputs, each for a particular class. A voting
                 scheme is then applied to these output values to
                 determine the class of the input object. The approach
                 is examined and compared with the standard genetic
                 programming approach on four multiclass object
                 classification tasks with increasing difficulty. The
                 results show that the new approach outperforms the
                 basic approach on these problems. A characteristic of
                 the proposed program structure is that it can easily
                 produce multiple outputs for multiclass object
                 classification problems, while still keeping the
                 advantages of the standard genetic programming approach
                 for easy crossover and mutation. This approach can
                 solve a multiclass object recognition problem using a
                 single evolved program in a single run.",
  notes =        "EvoISAP, EvoNET, EvoStar",
}

Genetic Programming entries for Mengjie Zhang Mark Johnston

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