Toward Co-Evolutionary Training of a Multi-Class Classifier

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "A. R. McIntyre and M. I. Heywood",
  title =        "Toward Co-Evolutionary Training of a Multi-Class
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "3",
  pages =        "2130--2137",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554958",
  size =         "8 pages",
  abstract =     "In this work the multi-class classification
                 capabilities of genetic programming (GP) are explored
                 in the context of a competitive co-evolutionary system,
                 in which a population of GP classifiers is trained
                 against an evolving population of trainers (exemplar
                 selectors) with the goal of reducing GP training time
                 for large multi-class classification problems.
                 Moreover, the niche-enabling mechanisms established in
                 the genetic algorithm (GA) literature, known as
                 crowding and sharing, are implemented for the
                 classifier population in order to provide multi-class
                 solutions from a single population in the same trial.
                 The results as presented in the paper indicate the
                 appropriateness of the competitive co-evolutionary
                 training approach under GP multi-class
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Andrew R McIntyre Malcolm Heywood