Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations

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

@InProceedings{castillo:2004:ueatsvtilmls,
  title =        "Using Evolutionary Algorithms to Suggest Variable
                 Transformations in Linear Model Lack-of-Fit
                 Situations",
  author =       "Flor Castillo and Jeff Sweeney and Wayne Zirk",
  pages =        "556--560",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
                 Computation",
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computing in the Process Industry",
  DOI =          "doi:10.1109/CEC.2004.1330906",
  abstract =     "When significant model lack of fit (LOF) is present in
                 a second-order linear regression model, it is often
                 difficult to propose the appropriate parameter
                 transformation that will make model LOF insignificant.
                 This paper presents the potential of genetic
                 programming (GP) symbolic regression for reducing or
                 eliminating significant second-order linear model LOF.
                 A case study in an industrial setting at The Dow
                 Chemical Company is presented to illustrate this
                 methodology.",
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",
}

Genetic Programming entries for Flor A Castillo Jeff Sweeney Wayne Zirk

Citations