Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{castillo:2002:gecco,
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author = "Flor A. Castillo and Ken A. Marshall and
James L. Green and Arthur K. Kordon",
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title = "Symbolic Regression In Design Of Experiments: {A} Case
Study With Linearizing Transformations",
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booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
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editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
R. Roy and D. Davis and R. Poli and K. Balakrishnan and
V. Honavar and G. Rudolph and J. Wegener and
L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and
E. Burke and N. Jonoska",
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year = "2002",
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pages = "1043--1047",
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address = "New York",
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publisher_address = "San Francisco, CA 94104, USA",
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month = "9-13 " # jul,
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publisher = "Morgan Kaufmann Publishers",
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keywords = "genetic algorithms, genetic programming, real world
applications, design of experiment (DoE), lack of fit,
linearizing transformations, symbolic regression",
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ISBN = "1-55860-878-8",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA194.pdf",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
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size = "6 pages",
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abstract = "The paper presents the potential of genetic
programming (GP)-generated symbolic regression for
linearising the response in statistical design of
experiments when significant Lack of Fit is detected
and no additional experimental runs are economically or
technically feasible because of extreme experimental
conditions. An application of this approach is
presented with a case study in an industrial setting at
The Dow Chemical Company.",
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notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
- }
Genetic Programming entries for
Flor A Castillo
Kenric A Marshall
James L Green
Arthur K Kordon
Citations