Bias-variance decomposition in Genetic Programming

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

@Article{Kowaliw:2016:omaths,
  author =       "Taras Kowaliw and Rene Doursat",
  title =        "Bias-variance decomposition in Genetic Programming",
  journal =      "Open Mathematics",
  year =         "2016",
  volume =       "14",
  number =       "1",
  pages =        "62--80",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Linear
                 Genetic Programming, Analysis of algorithms,
                 Bias-variance decomposition, Classification,
                 Computational learning theory, Evolutionary
                 computation, Learning and adaptive systems,
                 Non-parametric inference, Regression",
  ISSN =         "2391-5455",
  DOI =          "doi:10.1515/math-2016-0005",
  size =         "19 pages",
  abstract =     "We study properties of Linear Genetic Programming
                 (LGP) through several regression and classification
                 benchmarks. In each problem, we decompose the results
                 into bias and variance components, and explore the
                 effect of varying certain key parameters on the overall
                 error and its decomposed contributions. These
                 parameters are the maximum program size, the initial
                 population, and the function set used. We confirm and
                 quantify several insights into the practical usage of
                 GP, most notably that (a) the variance between runs is
                 primarily due to initialization rather than the
                 selection of training samples, (b) parameters can be
                 reasonably optimized to obtain gains in efficacy, and
                 (c) functions detrimental to evolvability are easily
                 eliminated, while functions well-suited to the problem
                 can greatly improve performance—therefore, larger and
                 more diverse function sets are always preferable.",
  notes =        "Institut des Systemes Complexes Paris Ile-de-France
                 (ISC-PIF), Centre National de la Recherche Scientifique
                 (CNRS UPS3611), 113 rue Nationale, 75013 Paris,
                 France

                 Informatics Research Centre, School of Computing,
                 Mathematics & Digital Technology, Manchester
                 Metropolitan University, John Dalton Building, Chester
                 Street, Manchester M1 5GD, UK",
}

Genetic Programming entries for Taras Kowaliw Rene Doursat

Citations