Nonlinear parametric regression in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yung-Keun Kwon and Sung-Soon Choi and Byung-Ro Moon",
  title =        "Nonlinear parametric regression in genetic
  booktitle =    "{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation",
  year =         "2006",
  editor =       "Maarten Keijzer and Mike Cattolico and Dirk Arnold and 
                 Vladan Babovic and Christian Blum and Peter Bosman and 
                 Martin V. Butz and Carlos {Coello Coello} and 
                 Dipankar Dasgupta and Sevan G. Ficici and James Foster and 
                 Arturo Hernandez-Aguirre and Greg Hornby and 
                 Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and 
                 Franz Rothlauf and Conor Ryan and Dirk Thierens",
  volume =       "1",
  ISBN =         "1-59593-186-4",
  pages =        "943--944",
  address =      "Seattle, Washington, USA",
  URL =          "",
  DOI =          "doi:10.1145/1143997.1144161",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "8-12 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming: Poster,
                 mathematical modelling/curve fitting, system
  size =         "2 pages",
  abstract =     "Genetic programming has been considered a promising
                 approach for function approximation since it is
                 possible to optimize both the functional form and the
                 coefficients. However, it is not easy to find an
                 optimal set of coefficients by using only
                 non-adjustable constant nodes in genetic programming.
                 To overcome the problem, there have been some studies
                 on genetic programming using adjustable parameters in
                 linear or non-linear models. Although the nonlinear
                 parametric model has a merit over the linear parametric
                 model, there have been few studies on it. In this
                 paper, we propose a nonlinear parametric genetic
                 programming which uses a nonlinear gradient method to
                 estimate parameters. The most notable feature in the
                 proposed genetic programming is that we design a
                 parameter attachment algorithm using as few redundant
                 parameters as possible.",
  notes =        "GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060",

Genetic Programming entries for Yung-Keun Kwon Sung-Soon Choi Byung-Ro Moon