Genetic program based data mining of fuzzy decision trees and methods of improving convergence and reducing bloat

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Smith:2007:SPIE,
  author =       "James F. {Smith, III} and Thanh Vu H. Nguyen",
  title =        "Genetic program based data mining of fuzzy decision
                 trees and methods of improving convergence and reducing
                 bloat",
  booktitle =    "SPIE Defense and Security 2007",
  year =         "2007",
  editor =       "Belur V. Dasarathy",
  volume =       "6570",
  address =      "USA",
  month =        "12-13 " # apr,
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 knowledge discovery, fuzzy logic, genetic program,
                 co-evolution",
  isbn13 =       "9780819466921",
  DOI =          "doi:10.1117/12.716973",
  size =         "12 pages",
  abstract =     "A data mining procedure for automatic determination of
                 fuzzy decision tree structure using a genetic program
                 (GP) is discussed. A GP is an algorithm that evolves
                 other algorithms or mathematical expressions.
                 Innovative methods for accelerating convergence of the
                 data mining procedure and reducing bloat are given. In
                 genetic programming, bloat refers to excessive tree
                 growth. It has been observed that the trees in the
                 evolving GP population will grow by a factor of three
                 every 50 generations. When evolving mathematical
                 expressions much of the bloat is due to the expressions
                 not being in algebraically simplest form. So a bloat
                 reduction method based on automated computer algebra
                 has been introduced. The effectiveness of this
                 procedure is discussed. Also, rules based on fuzzy
                 logic have been introduced into the GP to accelerate
                 convergence, reduce bloat and produce a solution more
                 readily understood by the human user. These rules are
                 discussed as well as other techniques for convergence
                 improvement and bloat control. Comparisons between
                 trees created using a genetic program and those
                 constructed solely by interviewing experts are made. A
                 new co-evolutionary method that improves the control
                 logic evolved by the GP by having a genetic algorithm
                 evolve pathological scenarios is discussed. The effect
                 on the control logic is considered. Finally, additional
                 methods that have been used to validate the data mining
                 algorithm are referenced.",
  notes =        "Data Mining, Intrusion Detection, Information
                 Assurance, and Data Networks Security",
}

Genetic Programming entries for James F Smith III ThanhVu Nguyen

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