Genetic Programming for Dynamic Chaotic Systems Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Katya Rodriguez-Vazquez and Peter J. Fleming",
  title =        "Genetic Programming for Dynamic Chaotic Systems
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "22--28",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective optimization, Chua's circuit, NARMA
                 difference equation model, complexity, difference
                 equation model representation, double scroll attractor,
                 dynamic chaotic systems modelling, dynamic
                 characteristic, fitness evaluation, fitness function,
                 genetic programming, hierarchical tree encoding,
                 identification, multiobjective function, multiobjective
                 optimisation problem, nondominated chaotic models,
                 nondominated model solutions, performance, statistical
                 validation, Chua's circuit, computational complexity,
                 difference equations, evolutionary computation,
                 identification, optimisation, time series",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.781903",
  abstract =     "This work presents an investigation into the use of
                 genetic programming (GP) applied to chaotic systems
                 modelling. A difference equation model representation
                 was proposed for being the basis of the hierarchical
                 tree encoding in GP. Based upon the NARMA difference
                 equation model and formulating the identification as a
                 multiobjective optimisation problem, Chua's circuit was
                 studied. The formulation of the GP fitness function,
                 defined as a multiobjective function, generated a set
                 of nondominated chaotic models. This approach
                 considered criteria related to the complexity,
                 performance and also statistical validation of the
                 models in the fitness evaluation. The final set of
                 non-dominated model solutions were able to capture the
                 dynamic characteristics of the system and reproduce the
                 chaotic motion of the double scroll attractor",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for Katya Rodriguez-Vazquez Peter J Fleming