Ecological Model Selection via Evolutionary Computation and Information Theory

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "James P. Hoffmann and Christopher D. Ellingwood and 
                 Osei M. Bonsu and Daniel E. Bentil",
  title =        "Ecological Model Selection via Evolutionary
                 Computation and Information Theory",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2004",
  volume =       "5",
  number =       "2",
  pages =        "229--241",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, model
                 selection, parsimony, complexity-based fitness,
                 variable-length representation",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1023/B:GENP.0000023690.71330.42",
  abstract =     "an evolutionary algorithm-based approach to model
                 selection and demonstrates its effectiveness in using
                 the information content of ecological data to choose
                 the correct model structure. Experiments with a
                 modified genetic algorithm are described that combine
                 parsimony with a novel gene regulation mechanism. This
                 combination creates evolvable switches that implement
                 functional variable-length genomes in the GA that allow
                 for simultaneous model selection and parameter fitting.
                 In effect, the GA orchestrates a competition among a
                 community of models. Parsimony is implemented via the
                 Akaike Information Criterion, and gene regulation uses
                 a modulo function to overload the gene values and
                 create an evolvable binary switch. The approach is
                 shown to successfully specify the correct model
                 structure in experiments with a nested set of
                 polynomial test models and complex biological
                 simulation models, even when Gaussian noise is added to
                 the data.",
  notes =        "Special Issue on Biological Applications of Genetic
                 and Evolutionary Computation Guest Editor(s): Wolfgang
                 Banzhaf , James Foster

                 (1) Botany, University of Vermont, Burlington, VT,

                 (2) Mathematics & Statistics, University of Vermont,
                 Burlington, VT, 05401-0086-3357",

Genetic Programming entries for James P Hoffmann Christopher D Ellingwood Osei M Bonsu Daniel E Bentil