Symbolic Regression Modeling of Blown Film Process Effects

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Symbolic Regression Modeling of Blown Film Process
  author =       "Arthur Kordon and Ching-Tai Lue",
  pages =        "561--568",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
  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.2004.1330907",
  abstract =     "The potential of symbolic regression for automatic
                 generation of process effects empirical models has been
                 explored on a real industrial case study. A novel
                 methodology based on nonlinear variable selection and
                 model derivation by Genetic Programming has been
                 defined and successfully applied for blown film process
                 effects modeling. The derived nonlinear models are
                 simple, have better performance than the linear models,
                 and predicted behavior in accordance with the process
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",

Genetic Programming entries for Arthur K Kordon Ching-Tai Lue