Coevolutionary bid-based genetic programming for problem decomposition in classification

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

  author =       "Peter Lichodzijewski and Malcolm I. Heywood",
  title =        "Coevolutionary bid-based genetic programming for
                 problem decomposition in classification",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2008",
  volume =       "9",
  number =       "4",
  pages =        "331--365",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Coevolution,
                 Problem decomposition, Teaming, Classification, SVM",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-008-9067-9",
  abstract =     "In this work a cooperative, bid-based, model for
                 problem decomposition is proposed with application to
                 discrete action domains such as classification. This
                 represents a significant departure from models where
                 each individual constructs a direct input-outcome map,
                 for example, from the set of exemplars to the set of
                 class labels as is typical under the classification
                 domain. In contrast, the proposed model focuses on
                 learning a bidding strategy based on the exemplar
                 feature vectors; each individual is associated with a
                 single discrete action and the individual with the
                 maximum bid wins the right to suggest its action. Thus,
                 the number of individuals associated with each action
                 is a function of the intra-action bidding behaviour.
                 Credit assignment is designed to reward correct but
                 unique bidding strategies relative to the target
                 actions. An advantage of the model over other teaming
                 methods is its ability to automatically determine the
                 number of and interaction between cooperative team
                 members. The resulting model shares several traits with
                 learning classifier systems and as such both approaches
                 are benchmarked on nine large classification problems.
                 Moreover, both of the evolutionary models are compared
                 against the deterministic Support Vector Machine
                 classification algorithm. Performance assessment
                 considers the computational, classification, and
                 complexity characteristics of the resulting solutions.
                 The bid-based model is found to provide simple yet
                 effective solutions that are robust to wide variations
                 in the class representation. Support Vector Machines
                 and classifier systems tend to perform better under
                 balanced datasets albeit resulting in black-box

Genetic Programming entries for Peter Lichodzijewski Malcolm Heywood