Co-Evolving Fuzzy Decision Trees and Scenarios

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

@InProceedings{smith:2008:cec,
  author =       "James F. {Smith, III}",
  title =        "Co-Evolving Fuzzy Decision Trees and Scenarios",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "3167--3176",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0700.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631227",
  abstract =     "A co-evolutionary data mining algorithm has been
                 invented that automatically generates decision logic in
                 the form of fuzzy decision trees (FDTs). The algorithm
                 initially uses a genetic program (GP) to mine a
                 database of scenarios to automatically create the fuzzy
                 logic. This is followed by the application of a genetic
                 algorithm (GA) that is used to search for pathological
                 scenarios (PS) that result in unsatisfactory
                 performance by the fuzzy logic found by the GP. The
                 fuzzy logic found in the previous step by the GP along
                 with failure criteria (FC) is used to form the fitness
                 function for the GA. If the GA fails to find
                 pathological scenarios then the co-evolution ends;
                 otherwise, the new scenarios are appended to the GP's
                 database followed by GP based data mining and a GA
                 scenario search. A detailed description of the
                 co-evolution of a fuzzy decision tree for real-time
                 control of unmanned air vehicles is provided. The
                 fitness functions for the GP, terminal set, function
                 set, and methods of accelerating convergence are
                 included. The fitness function for the GA and a method
                 of representing scenarios as chromosomes are given.
                 Simulations related to validation of the fuzzy logic
                 are discussed.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for James F Smith III

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