Assembling bloat control strategies in genetic programming for image noise reduction

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Assembling bloat control strategies in genetic
                 programming for image noise reduction",
  author =       "Keiko Ono and Yoshiko Hanada",
  publisher =    "IEEE",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2015-04-09",
  bibsource =    "DBLP,
  booktitle =    "ISDA",
  isbn13 =       "978-1-4799-7938-7",
  pages =        "113--118",
  URL =          "",
  DOI =          "doi:10.1109/ISDA.2014.7066279",
  abstract =     "We address the problem of controlling bloat in genetic
                 programming(GP) for image noise reduction. One of the
                 most basic nonlinear filters for image noise reduction
                 is the stack filter, and GP is suitable for estimating
                 the min-max function used for a stack filter. However,
                 bloat often occurs when the min-max function is
                 estimated with GP. In order to enhance image noise
                 reduction with GP, we extend the size-fair model GP,
                 and propose a novel bloat control method based on tree
                 size and frequent trees for image noise reduction,
                 where the frequent trees are the relatively small
                 subtrees appearing frequently among the population. By
                 using texture images with impulse noise, we demonstrate
                 that the size-fair model can achieve bloat control, and
                 performance improvement can be achieved through bloat
                 control based on tree size and frequent trees. Further,
                 we demonstrate that the proposed method outperforms a
                 typical image noise reduction method.",

Genetic Programming entries for Keiko Ono Yoshiko Hanada