A comparison of genetic programming variants for data classification

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

@InProceedings{EEH99b,
  author =       "Jeroen Eggermont and Agoston E. Eiben and 
                 Jano I. {van Hemert}",
  title =        "A comparison of genetic programming variants for data
                 classification",
  booktitle =    "Advances in Intelligent Data Analysis, Third
                 International Symposium, IDA-99",
  year =         "1999",
  editor =       "David J. Hand and Joost N. Kok and 
                 Michael R. Berthold",
  volume =       "1642",
  series =       "LNCS",
  email =        "jvhemert@cs.leidenuniv.nl",
  pages =        "281--290",
  address =      "Amsterdam, The Netherlands",
  publisher_address = "Berlin",
  month =        "9--11 " # aug,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 classification, data mining",
  URL =          "http://www.liacs.nl/~jeggermo/publications/ida99.ps.gz",
  URL =          "http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz",
  ISBN =         "3-540-66332-0",
  abstract =     "We report a comparative study on different variations
                 of genetic programming applied on binary data
                 classification problems. The first genetic programming
                 variant is weighting data records for calculating the
                 classification error and modifying the weights during
                 the run. Hereby the algorithm is defining its own
                 fitness function in an on-line fashion giving higher
                 weights to `hard' records. Another novel feature we
                 study is the atomic representation, where
                 `Booleanization' of data is not performed at the root,
                 but at the leafs of the trees and only Boolean
                 functions are used in the trees' body. As a third
                 aspect we look at generational and steady-state models
                 in combination of both features.",
  notes =        "IDA-99, Booleanization of inputs, ML: Australian
                 credit, German Credit, Heart Disease, Pima. steady
                 state. SAW-ing",
}

Genetic Programming entries for Jeroen Eggermont Gusz Eiben Jano I van Hemert

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