N-Version Genetic Programming: A Probabilistically Optimal Ensemble Approach

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

@PhdThesis{Imamura:thesis,
  author =       "Kosuke Imamura",
  title =        "{N}-Version Genetic Programming: A Probabilistically
                 Optimal Ensemble Approach",
  school =       "Department of Computer Science, University of Idaho",
  year =         "2002",
  address =      "Moscow, ID, USA",
  month =        "6 " # dec,
  keywords =     "genetic algorithms, genetic programming, NVGP",
  URL =          "http://phdtree.org/pdf/23560496-n-version-genetic-programming-a-probabilistically-optimal-ensemble-approach/",
  URL =          "http://www.uidaho.edu/engr/cs/Research/thesesanddissertations",
  URL =          "http://search.proquest.com/docview/288080102",
  size =         "96 pages",
  abstract =     "This research provides a method to enhance accuracy
                 and reduce performance fluctuation of programs produced
                 by genetic programming by combining individual evolved
                 programs into robust ensembles. More effective
                 ensembles have fewer correlated faulty outputs.
                 Therefore, current ensemble techniques focus on
                 diversity pressures to reduce correlated faults among
                 the ensemble members. However, whether or not an
                 optimal ensemble is formed through these pressures is
                 unknown, simply because ensemble optimality is
                 undefined. We define the behavioural diversity of an
                 ensemble of imperfect programs as the degree to which
                 the ensemble failure rate deviates from what one would
                 expect if fault occurrences were statistically
                 independent. Given this metric, we form an ensemble by
                 selecting individuals that exhibit this diversity from
                 a large pool of evolved programs and combining their
                 outputs into a single ensemble output. Classification
                 or prediction problems benefit the most from this
                 research. We have validated our approach by showing
                 statistically significant improvements when applied to
                 a DNA segment classification problem.",
  notes =        "Supervisor: James A. Foster

                 UMI Microform 3080258

                 E-coli promoter recognition",
}

Genetic Programming entries for Kosuke Imamura

Citations