Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Doucette:2012:GPEM,
  author =       "John A. Doucette and Andrew R. McIntyre and 
                 Peter Lichodzijewski and Malcolm I. Heywood",
  title =        "Symbiotic coevolutionary genetic programming: a
                 benchmarking study under large attribute spaces",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "13",
  number =       "1",
  pages =        "71--101",
  month =        mar,
  note =         "Special Section on Evolutionary Algorithms for Data
                 Mining",
  keywords =     "genetic algorithms, genetic programming, Feature
                 subspace selection, Problem decomposition, Symbiosis,
                 Coevolution, Model complexity, Classification",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-011-9151-4",
  size =         "31 pages",
  abstract =     "Classification under large attribute spaces represents
                 a dual learning problem in which attribute subspaces
                 need to be identified at the same time as the
                 classifier design is established. Embedded as opposed
                 to filter or wrapper methodologies address both tasks
                 simultaneously. The motivation for this work stems from
                 the observation that team based approaches to Genetic
                 Programming (GP) have the potential to design multiple
                 classifiers per class. each with a potentially unique
                 attribute subspace. without recourse to filter or
                 wrapper style preprocessing steps. Specifically,
                 competitive coevolution provides the basis for scaling
                 the algorithm to data sets with large instance counts;
                 whereas cooperative coevolution provides a framework
                 for problem decomposition under a bid-based model for
                 establishing program context. Symbiosis is used to
                 separate the tasks of team/ensemble composition from
                 the design of specific team members. Team composition
                 is specified in terms of a combinatorial search
                 performed by a Genetic Algorithm (GA); whereas the
                 properties of individual team members and therefore
                 subspace identification is established under an
                 independent GP population. Teaming implies that the
                 members of the resulting ensemble of classifiers should
                 have explicitly non-overlapping behaviour. Performance
                 evaluation is conducted over data sets taken from the
                 UCI repository with 649-102,660 attributes and 2-10
                 classes. The resulting teams identify attribute spaces
                 1-4 orders of magnitude smaller than under the original
                 data set. Moreover, team members generally consist of
                 less than 10 instructions; thus, small attribute
                 subspaces are not being traded for opaque models.",
  affiliation =  "David R. Cheriton School of Computer Science,
                 University of Waterloo, Waterloo, ON, Canada",
}

Genetic Programming entries for John Doucette Andrew R McIntyre Peter Lichodzijewski Malcolm Heywood

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