Implementing Linear Models in Genetic Programming

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

@Article{yeun_2004_tec,
  author =       "Yun-Seog Yeun and Won-Sun Ruy and Young-Soon Yang and 
                 Nam-Joon Kim",
  title =        "Implementing Linear Models in Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2004",
  volume =       "8",
  number =       "6",
  pages =        "542--566",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Directional
                 derivative-based smoothing (DDBS), linear model,
                 minimum description length (MDL) principle, polynomial,
                 symbolic processing",
  URL =          "http://members.kr.inter.net/yyshuj/paper/pre-lm-gp.pdf",
  DOI =          "doi:10.1109/TEVC.2004.836818",
  size =         "25 pages",
  abstract =     "We deal with linear models of genetic programming (GP)
                 for regression or approximation problems when given
                 learning samples are not sufficient. The linear model,
                 which is a function of unknown parameters, is built
                 through extracting all possible base functions from the
                 standard GP tree by a symbolic processing algorithm.
                 The major advantage of a linear model in GP is that its
                 parameters can be estimated by the ordinary least
                 square (OLS) method and a good model can be selected by
                 applying the modern minimum description length (MDL)
                 principle, while the nonlinearity necessary to handle
                 the given problem is effectively maintained by
                 indirectly evolving and finding various forms of base
                 functions. In addition to a standard linear model
                 consisting of mathematical functions, one variant of a
                 linear model, which can be built using low-order Taylor
                 series and can be converted into the standard form of a
                 polynomial, is considered in this paper. With small
                 samples, GP frequently shows the abnormal behaviors
                 such as extreme large peaks or odd-looking
                 discontinuities at the points away from sample points.
                 To overcome this problem, a directional
                 derivative-based smoothing (DDBS) method, which is
                 incorporated into the OLS method, is introduced
                 together with the fitness function that is based on
                 MDL, reflecting the effects of DDBS. Also, two
                 illustrative examples and three engineering
                 applications are presented.",
}

Genetic Programming entries for Yun Seog Yeun Won-Sun Ruy Young-Soon Yang Nam-Joon Kim

Citations