Symbolic Regression Modeling of Blown Film Process Effects
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{kordon:2004:srmobfpe,
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title = "Symbolic Regression Modeling of Blown Film Process
Effects",
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author = "Arthur Kordon and Ching-Tai Lue",
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pages = "561--568",
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booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
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year = "2004",
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publisher = "IEEE Press",
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month = "20-23 " # jun,
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address = "Portland, Oregon",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Computing in the Process Industry",
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DOI = "doi:10.1109/CEC.2004.1330907",
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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.",
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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