On Multi-class Classification by Way of Niching

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "A. R. McIntyre and M. I. Heywood",
  title =        "On Multi-class Classification by Way of Niching",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "581--592",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  URL =          "http://users.cs.dal.ca/~mheywood/X-files/Publications/andy-GECCO04.pdf",
  DOI =          "doi:10.1007/b98645",
  size =         "12",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In recent literature, the niche enabling effects of
                 crowding and the sharing algorithms have been
                 systematically investigated in the context of Genetic
                 Algorithms and are now established evolutionary methods
                 for identifying optima in multi-modal problem domains.
                 In this work, the niching metaphor is methodically
                 explored in the context of a simultaneous
                 multi-population GP classifier in order to investigate
                 which (if any) properties of traditional sharing and
                 crowding algorithms may be portable in arriving at a
                 naturally motivated niching GP. For this study, the
                 niching mechanisms are implemented in Grammatical
                 Evolution to provide multi-category solutions from the
                 same population in the same trial. Each member of the
                 population belongs to a different niche in the GE
                 search space corresponding to the data classes. The set
                 of best individuals from each niche are combined
                 hierarchically and used for multi-class classification
                 on the familiar multi-class UCI data sets of Iris and
                 Wine. A distinct preference for Sharing as opposed to
                 Crowding is demonstrated with respect to population
                 diversity during evolution and niche classification
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)",

Genetic Programming entries for Andrew R McIntyre Malcolm Heywood