Statistical Evaluation of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{kaboudan:1999:seGP,
  author =       "M. A. Kaboudan",
  title =        "Statistical Evaluation of Genetic Programming",
  booktitle =    "Fifth International Conference: Computing in Economics
                 and Finance",
  year =         "1999",
  editor =       "David A. Belsley and Christopher F. Baum",
  pages =        "148",
  address =      "Boston College, MA, USA",
  month =        "24-26 " # jun,
  organisation = "Society for Computational Economics",
  note =         "Book of Abstracts",
  keywords =     "genetic algorithms, genetic programming, GP-QUICK",
  broken =       "http://www.lv.psu.edu/mak7/GP-Stat.htm",
  URL =          "http://EconPapers.repec.org/RePEc:sce:scecf9:1031",
  URL =          "http://fmwww.bc.edu/CEF99/doc/pgm.html",
  size =         "1 page",
  abstract =     "A recent advance in genetic computations is the
                 heuristic prediction model (symbolic regression), which
                 have received little statistical scrutiny. Diagnostic
                 checks of genetically evolved models (GEMs) as a
                 forecasting method are therefore essential. This
                 requires assessing the statistical properties of errors
                 produced by GEMs. Since the predicted models and their
                 forecasts are produced artificially by a computer
                 program, little controls the final model specification.
                 However, it is of interest to understand the final
                 specification and to know the statistical
                 characteristics of its errors, particularly if
                 artificially produced models furnish better forecasts
                 than humanly conceived ones. This paper's main concern
                 is the statistical analysis of errors from genetically
                 evolved models. Genetic programming (GP) is one of two
                 computational algorithms for evolving regression
                 models, the other being evolutionary programming (EP).
                 GP-QUICK computer code written in C ++ evolves the
                 regression models for this study. GP-QUICK replicates
                 an original GP program in LISP by Koza. Both are
                 designed to evolve regression models randomly, finding
                 one that replicates the series' data-generating process
                 best. Prediction errors from GP evolved regression
                 models are tested for whiteness (or autocorrelation)
                 and for normality. Well-established diagnostic tools
                 for linear time-series modeling apply also to nonlinear
                 models. Only diagnostic methods using errors without
                 having to replicate the models that produced them are
                 selected and applied to series. This restriction is
                 avoids reproducing the resulting genetically evolved
                 equations. These equations are generated by a random
                 selection mechanism almost impossible to replicate with
                 GP unless the process is deterministic, and they are
                 usually too complex for standard statistical software
                 to reproduce and analyze. The diagnostic methods are
                 selected for their simplicity and speed of execution
                 without sacrificing reliability. This paper contains
                 four other sections. One presents the diagnostic tools
                 to determine the statistical properties of residuals
                 produced by GEMs. Residuals from evolved models
                 representing systems with known characteristics are
                 used to evaluate the statistical performance of GEMs.
                 Another furnishes six data-generating processes
                 representing linear, linear-stochastic, nonlinear,
                 nonlinear-stochastic, and pseudo-random systems for
                 which models are evolved and residuals computed. The
                 final contains those residuals' diagnostics. Diagnostic
                 tools include the Kolmogorov-Smirnov test for whiteness
                 developed by Durbin (1969) in addition to statistical
                 testing of the null hypotheses that the fitted
                 residuals' mean, skewness, and kurtosis are
                 independently equal to zero. Conclusions and future
                 research are given.",
  notes =        "CEF'99 RePEc:sce:scecf9:1031 23 Nov 1999: Our printers
                 barf if given GP-Stat.prn

                 22 Aug 2004
                 http://ideas.repec.org/p/sce/scecf9/1031.html CEF
                 number 1031",
}

Genetic Programming entries for Mahmoud A Kaboudan

Citations