Genetically evolved models and normality of their fitted residuals

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "M. A. Kaboudan",
  title =        "Genetically evolved models and normality of their
                 fitted residuals",
  journal =      "Journal of Economic Dynamics and Control",
  year =         "2001",
  volume =       "25",
  number =       "11",
  pages =        "1719--1749",
  month =        "1 " # nov,
  organisation = "Society for Computational Economics",
  email =        "",
  keywords =     "genetic algorithms, genetic programming, Model
                 evaluation, Sunspot numbers, Canadian lynx data",
  URL =          "",
  DOI =          "doi:10.1016/S0165-1889(00)00004-X",
  size =         "31 pages",
  abstract =     "This paper evaluates performance of genetically
                 evolved models. GPQuick, a genetic programming software
                 written in C++, is used to evolve best-fit regression
                 models for simulated and real world data. Simulated
                 data are twelve time series with different but known
                 dynamical structures. Predicted values from best models
                 are compared with originally simulated data and the
                 residuals are statistically evaluated. The results
                 suggest that genetic programming approximates less
                 complex and less noisy data better than it does more
                 complex and noisy data. GPQuick is then used to evolve
                 models of real world data extracted from Canadian lynx
                 and sunspot numbers.",
  notes =        "JEL Classification: C63; C45; C52. cf. CEF'2000.",

Genetic Programming entries for Mahmoud A Kaboudan