Novelty Detection + Coevolution = Automatic Problem Decomposition: A Framework for Scalable Genetic Programming Classifiers

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

@PhdThesis{McIntyre:thesis,
  author =       "Andrew R. McIntyre",
  title =        "Novelty Detection + Coevolution = Automatic Problem
                 Decomposition: A Framework for Scalable Genetic
                 Programming Classifiers",
  school =       "Dalhousie University",
  year =         "2007",
  address =      "Halifax, Nova Scotia, Canada",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://projectx25.cs.dal.ca/home/docs/armcintyre_phd_thesis_final.pdf",
  size =         "442 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 readily applied to
                 large data sets (tens or hundreds of thousands of
                 exemplars) without recourse to hardware-specific
                 speedups. A key departure from the canonical GP
                 approach to classification involves expressing the
                 output of GP in terms of a non-binary, local membership
                 function (e.g., a Gaussian). Decomposition is achieved
                 by 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 as opposed to a singular `super'
                 individual.

                 Although multi-objective methods have previously been
                 reported for GP classification domains, we explicitly
                 formulate the objectives for cooperative behavior.
                 Without this the user is left to choose a single
                 individual as the overall solution from a front of
                 solutions. This work is able to use the entire front of
                 solutions without recourse to heuristics for selecting
                 one individual over another or duplicating behaviors
                 between different classifiers.

                 Extensive benchmarking was performed against
                 alternative frameworks for classification including
                 Genetic Programming, Neural Networks, and deterministic
                 methods. In contrast to classifiers evolved using
                 competitive coevolution alone, we demonstrate the
                 ability of the proposed coevolutionary model to provide
                 a non-overlapping decomposition or association between
                 learners and exemplars, while returning statistically
                 significant improvements in classifier performance. In
                 the case of the Neural Network methods, benchmarking is
                 conducted against the more challenging second order
                 neural learning algorithm of conjugate gradient
                 optimization (previous comparisons limit Neural
                 Networks to first order methods). The proposed
                 evolutionary method was often significantly better than
                 the non-linear Neural Network, whereas the linear model
                 tended to work well or not at all. In effect, the
                 evolutionary paradigm provided a more robust model for
                 searching the space of non-linear models than provided
                 under the neural gradient decent paradigm. With respect
                 to deterministic methods, the problem of benchmarking
                 stochastic versus deterministic algorithms is first
                 addressed, with a new methodology established for
                 making such comparisons. The ensuing comparison
                 demonstrated that the evolutionary algorithms remain
                 competitive with most data sets appearing to benefit
                 from the proposed evolutionary methodology.",
  notes =        "http://projectx25.cs.dal.ca/home/?pg=research

                 Advisor: Dr. Malcolm I. Heywood (Dalhousie University,
                 Faculty of Computer Science)

                 External Examiner: Dr. Una-May O'Reilly (MIT Computer
                 Science and Artificial Intelligence Lab)

                 Graduate Committee: Dr. Evangelos E. Milios and Dr.
                 Syed Sibte Raza Abidi (Dalhousie University, Faculty of
                 Computer Science)",
}

Genetic Programming entries for Andrew R McIntyre

Citations