Using unrestricted loops in genetic programming for image classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Jan Larres and Mengjie Zhang and Will N Browne",
  title =        "Using unrestricted loops in genetic programming for
                 image classification",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Loops are an important part of classic programming
                 techniques, but are rarely used in genetic programming.
                 This paper presents a method of using unrestricted,
                 i.e. nesting, loops to evolve programs for image
                 classification tasks. Contrary to many other
                 classification methods where pre-extracted features are
                 typically used, we perform calculations on image
                 regions determined by the loops. Since the loops can be
                 nested, these regions may depend on previously computed
                 regions, thereby allowing a simple version of
                 conditional evaluation. The proposed GP approach with
                 unrestricted loops is examined and compared with the
                 canonical GP method without loops and the GP approach
                 with restricted loops on one synthesised character
                 recognition problem and two texture classification
                 problems. The results suggest that unrestricted loops
                 can have an advantage over the other two methods in
                 certain situations for image classification.",
  DOI =          "doi:10.1109/CEC.2010.5586305",
  notes =        "WCCI 2010. Also known as \cite{5586305}",

Genetic Programming entries for Jan Larres Mengjie Zhang Will N Browne