Multi-Objective Competitive Coevolution for Efficient GP Classifier Problem Decomposition

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

  author =       "A. R. McIntyre and M. I. Heywood",
  title =        "Multi-Objective Competitive Coevolution for Efficient
                 GP Classifier Problem Decomposition",
  booktitle =    "Proceedings of the IEEE International Conference on
                 Systems, Man, and Cybernetics",
  year =         "2007",
  pages =        "1930--1937",
  address =      "Montreal",
  month =        "7-10 " # oct,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0991-8",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/ICSMC.2007.4414009",
  size =         "8 pages",
  abstract =     "A novel approach to the classification of large and
                 unbalanced multi-class data sets is presented where the
                 widely acknowledged issues of scalability, solution
                 transparency, and problem decomposition are addressed
                 simultaneously within the context of the Genetic
                 Programming (GP) paradigm. A cooperative coevolutionary
                 training environment that employs multi-objective
                 evaluation provides the basis for problem decomposition
                 and reduced solution complexity, while scalability is
                 achieved through a Pareto competitive coevolutionary
                 framework, allowing the system to be applied to large
                 data sets (tens or hundreds of thousands of exemplars)
                 without recourse to hardware-specific speedups.
                 Moreover, a key departure from the canonical GP
                 approach to classification is used in which the output
                 of GP is expressed in terms of a non-binary, local
                 membership function (e.g. a Gaussian), where it is no
                 longer necessary for an expression to represent an
                 entire class. Decomposition is then achieved through
                 reformulating the classification problem as one of
                 cluster consistency, where an appropriate subset of the
                 training patterns can be associated with each
                 individual such that problems are solved by several
                 specialist classifiers rather than by a single super
  notes =        "

                 NB armcnty_SMC07.pdf is 21 pages

                 Also known as \cite{4414009}",

Genetic Programming entries for Andrew R McIntyre Malcolm Heywood