A fitness case strategy in genetic programming to improve system identification

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Marco A. Pacheco and Mario Graff and Jamie Cerda",
  title =        "A fitness case strategy in genetic programming to
                 improve system identification",
  booktitle =    "IEEE International Autumn Meeting on Power,
                 Electronics and Computing (ROPEC 2013)",
  year =         "2013",
  month =        "13-15 " # nov,
  keywords =     "genetic algorithms, genetic programming, Eureqa",
  DOI =          "doi:10.1109/ROPEC.2013.6702728",
  abstract =     "This article discusses the use of genetic programming
                 for system identification. To this end, several
                 experiments have been using observations obtained from
                 a power transformer. The proposed strategy is to
                 maximise the likelihood of convergence when searching
                 for the model of a particular system. A traditional
                 strategy for system identification in Genetic
                 Programming is to take all the observations and
                 evaluate the process of evolution to find a system
                 model instance. Contrary to this, the proposed
                 methodology is based on a partial subset of the
                 observations, and then this subset is incremented until
                 reaching the total set of observations. Furthermore,
                 for comparison purposes we have used Eureqa, an open
                 genetic programming based software tool for system
  notes =        "In spanish.

                 Also known as \cite{6702728}",

Genetic Programming entries for Marco A Pacheco Mario Graff Guerrero Jamie Cerda