Evolving Fuzzy Rule Based Classifiers with GA-P: A Grammatical Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Santiago Garcia and Fermin Gonzalez and 
                 Luciano Sanchez",
  title =        "Evolving Fuzzy Rule Based Classifiers with {GA-P}: A
                 Grammatical Approach",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and 
                 William B. Langdon and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "203--210",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=203",
  DOI =          "doi:10.1007/3-540-48885-5_17",
  abstract =     "Genetic Programming can be used to evolve Fuzzy
                 Rulebased classifiers [7]. Fuzzy GP depends on a
                 grammar defining valid expressions of fuzzy
                 classifiers, and guarantees that all individuals in the
                 population are valid instances of it all along the
                 evolution process. This is accomplished by restricting
                 crossover and mutation so that they only take place at
                 points of the derivation tree representing the same
                 non-terminal, thus generating valid subtrees [13].

                 In Fuzzy GP, terminal symbols are fuzzy constants and
                 variables that are chosen beforehand. In this work we
                 propose a method for evolving both fuzzy membership
                 functions of the variables and the Rule Base. Our
                 method extends the GA-P hybrid method [6] by
                 introducing a new grammar with two functional parts,
                 one for the Fuzzy Rule Base (GP Part), and the other
                 for the constants that define the shapes of the fuzzy
                 sets involved in the Fuzzy Rule Base (GA Part). We have
                 applied this method to some classical benchmarks taken
                 from the collection of test data at the UCI Repository
                 of Machine Learning Databases [9].",
  notes =        "EuroGP'99, part of \cite{poli:1999:GP}

                 Combination of grammar based GP and GA-P with fuzzy
                 rules. UCI machine learning databases

                 First author is Santiago Garcia Carbajal",

Genetic Programming entries for Santiago Garcia Carbajal Fermin Gonzalez Martinez Luciano Sanchez