Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Buchsbaum:2007:cec,
  author =       "Thomas Buchsbaum",
  title =        "Toward a Winning GP Strategy for Continuous Nonlinear
                 Dynamical System Identification",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1269--1275",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1490.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424616",
  abstract =     "System identification is the scientific art of
                 building models from data. Good models are of essential
                 importance in many areas of science and industry.
                 Models are used to analyse, simulate, and predict
                 systems and their states. Model structure selection and
                 estimation of the model parameters with respect to a
                 chosen criterion of fit are essential parts of the
                 identification process. In this article, we investigate
                 the suitability of genetic programming for creating
                 continuous nonlinear state-space models from noisy time
                 series data. We introduce methodologies from the field
                 of chaotic time series estimation and present concepts
                 for integrating them into a genetic programming system.
                 We show that even small changes of the fitness
                 evaluation approach may lead to a significantly
                 improved performance. In combination with
                 multiobjective optimisation, a multiple shooting
                 approach is able to create powerful models from noisy
                 data.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Thomas Buchsbaum

Citations