A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems

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

@InProceedings{Berlanga:2008:GEFS,
  author =       "Francisco Jose Berlanga and Maria Jose {del Jesus} and 
                 Francisco Herrera",
  title =        "A novel genetic cooperative-competitive fuzzy rule
                 based learning method using genetic programming for
                 high dimensional problems",
  booktitle =    "3rd International Workshop on Genetic and Evolving
                 Fuzzy Systems, GEFS 2008",
  year =         "2008",
  month =        "4-7 " # mar,
  address =      "Witten-Boommerholz, Germany",
  pages =        "101--106",
  keywords =     "genetic algorithms, genetic programming, genetic
                 cooperative-competitive fuzzy rule based learning
                 method, high dimensional classification problems, high
                 dimensional problems, token competition mechanism,
                 fuzzy set theory, knowledge based systems, learning
                 (artificial intelligence)",
  DOI =          "doi:10.1109/GEFS.2008.4484575",
  abstract =     "In this contribution, we present GP-COACH, a novel GFS
                 based on the cooperative-competitive learning approach,
                 that uses genetic programming to code fuzzy rules with
                 a different number of variables, for getting compact
                 and accurate rule bases for high dimensional problems.
                 GP-COACH learns disjunctive normal form rules
                 (generated by means of a context-free grammar) and uses
                 a token competition mechanism to maintain the diversity
                 of the population. It makes the rules compete and
                 cooperate among themselves, giving out a compact set of
                 fuzzy rules that presents a good performance. The good
                 results obtained in an experimental study involving
                 several high dimensional classification problems
                 support our proposal.",
  notes =        "Also known as \cite{4484575}",
}

Genetic Programming entries for Francisco Jose Berlanga Maria Jose del Jesus Francisco Herrera

Citations