Multiobjective GP for Human-Understandable Models: A Practical Application

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Katya Rodriguez-Vazquez and Peter J. Fleming",
  title =        "Multiobjective GP for Human-Understandable Models: A
                 Practical Application",
  booktitle =    "Multiobjective Problem Solving from Nature: from
                 concepts to applications",
  publisher =    "Springer",
  year =         "2008",
  editor =       "Joshua Knowles and David Corne and Kalyanmoy Deb",
  series =       "Natural Computing",
  chapter =      "10",
  pages =        "201--218",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-72963-1",
  DOI =          "doi:10.1007/978-3-540-72964-8_10",
  abstract =     "The work presented in this chapter is concerned with
                 the identification and modelling of nonlinear dynamical
                 systems using multiobjective evolutionary algorithms
                 (MOEAs). This problem involves the processes of
                 structure selection, parameter estimation, model
                 performance and model validation and defines a complex
                 solution space. Evolutionary algorithms (EAs), in
                 particular genetic programming (GP), are found to
                 provide a way of evolving models to solve this
                 identification and modelling problem, and their use is
                 extended to encompass multiobjective functions.
                 Multiobjective genetic programming (MOGP) is then
                 applied to multiple conflicting objectives in order to
                 yield a set of simple and valid human-understandable
                 models which can reproduce the behaviour of a given
                 unknown system.",
  notes =        "",

Genetic Programming entries for Katya Rodriguez-Vazquez Peter J Fleming