Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{castillo:2004:ueatsvtilmls,

• title = "Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations",
• author = "Flor Castillo and Jeff Sweeney and Wayne Zirk",
• pages = "556--560",
• 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.2004.1330906",
• abstract = "When significant model lack of fit (LOF) is present in a second-order linear regression model, it is often difficult to propose the appropriate parameter transformation that will make model LOF insignificant. This paper presents the potential of genetic programming (GP) symbolic regression for reducing or eliminating significant second-order linear model LOF. A case study in an industrial setting at The Dow Chemical Company is presented to illustrate this methodology.",
• notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.",

}

Genetic Programming entries for Flor A Castillo Jeff Sweeney Wayne Zirk

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