Genetic Programming Fuzzy Rule Extractor Using Class Preserving Representation

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

  author =       "Jiri Kubalik and Leon Rothkrantz and Jiri Lazansky",
  title =        "Genetic Programming Fuzzy Rule Extractor Using Class
                 Preserving Representation",
  booktitle =    "BNAIC",
  year =         "2001",
  pages =        "25--26 October",
  publisher_address = "De Rode Hoed, Amsterdam, Holland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "This paper describes a genetic programming approach to
                 the construction of fuzzy classification system with
                 if-then fuzzy rules. Recently many research studies
                 were focusing on evolutionary techniques for
                 automatically extracting fuzzy rules from data. In this
                 paper we present a method based on genetic programming
                 with a special structure preserving representation and
                 special rule base adjusting operators working on it.
                 First results show that the new features added to
                 standard GP extractor considerably improve both
                 performance and comprehensibility of found fuzzy
  notes =        "",

Genetic Programming entries for Jiri Kubalik Leon J M Rothkrantz Jiri Lazansky