Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations

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

@InProceedings{castillo:2002:gecco,

• author = "Flor A. Castillo and Ken A. Marshall and James L. Green and Arthur K. Kordon",
• title = "Symbolic Regression In Design Of Experiments: {A} Case Study With Linearizing Transformations",
• booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference",
• 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",
• year = "2002",
• pages = "1043--1047",
• address = "New York",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "9-13 " # jul,
• publisher = "Morgan Kaufmann Publishers",
• keywords = "genetic algorithms, genetic programming, real world applications, design of experiment (DoE), lack of fit, linearizing transformations, symbolic regression",
• ISBN = "1-55860-878-8",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA194.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
• size = "6 pages",
• 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.",
• 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

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