Dynamic Modelling Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "M. Hinchliffe and M. Willis",
  title =        "Dynamic Modelling Using Genetic Programming",
  booktitle =    "Proceedings of the 15th IFAC World Congress",
  year =         "2002",
  editor =       "Luis Basanez and Juan A. {de la Puente}",
  pages =        "441--441",
  address =      "Barcelona, Spain",
  organisation = "The international federation of automatic control",
  publisher =    "Elsevier",
  keywords =     "genetic algorithms, genetic programming, dynamic
                 modelling, multi-objective optimisation",
  URL =          "http://www.ifac-papersonline.net/Detailed/26074.html",
  DOI =          "doi:10.3182/20020721-6-ES-1901.00443",
  abstract =     "In this contribution we demonstrate how a Single
                 Objective Genetic Programming (SOGP) and a
                 Multi-Objective Genetic Programming (MOGP) algorithm
                 can be used to evolve accurate input-output models of
                 dynamic processes. Having described the algorithms, two
                 case studies are used to compare their performance with
                 that of Filter-Based Neural Networks (FBNNs). For the
                 examples given, the models generated using GP have
                 comparable prediction performance to the FBNN. However,
                 performance with respect to additional modelling
                 criteria can be improved using the MOGP algorithm.",
  notes =        "cited in \cite{hinchliffe:thesis}",

Genetic Programming entries for Mark P Hinchliffe Mark J Willis