Variable Selection in Industrial Datasets using Pareto Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Guido Smits and Arthur Kordon and 
                 Katherine Vladislavleva and Elsa Jordaan and Mark Kotanchek",
  title =        "Variable Selection in Industrial Datasets using Pareto
                 Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice {III}",
  year =         "2005",
  editor =       "Tina Yu and Rick L. Riolo and Bill Worzel",
  volume =       "9",
  series =       "Genetic Programming",
  chapter =      "6",
  pages =        "79--92",
  address =      "Ann Arbor",
  month =        "12-14 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 Regression, Variable Selection, Pareto GP",
  ISBN =         "0-387-28110-X",
  DOI =          "doi:10.1007/0-387-28111-8_6",
  size =         "14 pages",
  abstract =     "This chapter gives an overview, based on the
                 experience from the Dow Chemical Company, of the
                 importance of variable selection to build robust models
                 from industrial datasets. A quick review of variable
                 selection schemes based on linear techniques is given.
                 A relatively simple fitness inheritance scheme is
                 proposed to do nonlinear sensitivity analysis that is
                 especially effective when combined with Pareto GP. The
                 method is applied to two industrial datasets with good
  notes =        "part of \cite{yu:2005:GPTP} Published Jan 2006 after
                 the workshop",

Genetic Programming entries for Guido F Smits Arthur K Kordon Ekaterina (Katya) Vladislavleva Elsa Jordaan Mark Kotanchek