Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles

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

@InProceedings{DBLP:conf/ideal/BrazierRW04,
  author =       "Karl J. Brazier and Graeme Richards and Wenjia Wang",
  title =        "Implicit Fitness Sharing Speciation and Emergent
                 Diversity in Tree Classifier Ensembles",
  booktitle =    "Intelligent Data Engineering and Automated Learning -
                 IDEAL 2004, 5th International Conference, Proceedings",
  year =         "2004",
  pages =        "333--338",
  editor =       "Zheng Rong Yang and Richard M. Everson and Hujun Yin",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3177",
  ISBN =         "3-540-22881-0",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  address =      "Exeter, UK",
  month =        aug # " 25-27",
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3177&spage=333",
  DOI =          "doi:10.1007/b99975",
  abstract =     "Implicit fitness sharing is an approach to the
                 stimulation of speciation in evolutionary computation
                 for problems where the fitness of an individual is
                 determined as its success rate over a number trials
                 against a collection of succeed/fail tests. By fixing
                 the reward available for each test, individuals
                 succeeding in a particular test are caused to depress
                 the size of one another's fitness gain and hence
                 implicitly co-operate with those succeeding in other
                 tests. An important class of problems of this form is
                 that of attribute-value learning of classifiers. Here,
                 it is recognised that the combination of diverse
                 classifiers has the potential to enhance performance in
                 comparison with the use of the best obtainable
                 individual classifiers. However, proposed prescriptive
                 measures of the diversity required have inherent
                 limitations from which we would expect the diversity
                 emergent from the self-organisation of speciating
                 evolutionary simulation to be free. The approach was
                 tested on a number of the popularly used real-world
                 data sets and produced encouraging results in terms of
                 accuracy and stability.",
  notes =        "http://www.dcs.ex.ac.uk/ideal04/

                 a) Cleveland heart data b) Thyroid data c) Pima Indians
                 diabetes data d) E. coli data

                 ",
}

Genetic Programming entries for Karl J Brazier Graeme Richards Wenjia Wang

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