Symbolic Regression Modeling of Blown Film Process Effects

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{kordon:2004:srmobfpe,

• title = "Symbolic Regression Modeling of Blown Film Process Effects",
• author = "Arthur Kordon and Ching-Tai Lue",
• pages = "561--568",
• 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.2004.1330907",
• 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 physics.",
• notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.",

}

Genetic Programming entries for Arthur K Kordon Ching-Tai Lue

Citations