A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming

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

  author =       "Brian Carse and Anthony G. Pipe",
  title =        "A Framework for Evolving Fuzzy Classifier Systems
                 Using Genetic Programming",
  booktitle =    "Proceedings of the Fourteenth International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2001",
  editor =       "Ingrid Russell and John F. Kolen",
  pages =        "465--469",
  address =      "Key West, Florida, USA",
  month =        may # " 21-23",
  publisher =    "AAAI Press",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-57735-133-9",
  URL =          "http://www.aaai.org/Papers/FLAIRS/2001/FLAIRS01-089.pdf",
  size =         "5 pages",
  abstract =     "A fuzzy classifier system framework is proposed which
                 employs a tree-based representation for fuzzy rule
                 (classifier) antecedents and genetic programming for
                 fuzzy rule discovery. Such a rule representation is
                 employed because of the expressive power and generality
                 it endows to individual rules. The framework proposes
                 accuracy-based fitness for individual fuzzy classifiers
                 and employs evolutionary competition between
                 simultaneously matched classifiers. The evolutionary
                 algorithm (GP) is therefore searching for compact fuzzy
                 rule bases which are simultaneously general, accurate
                 and co-adapted. Additional extensions to the proposed
                 framework are suggested",

Genetic Programming entries for Brian Carse Anthony Pipe