Using evolutionary algorithms to suggest variable transformations in linear model lack-of-fit situations

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 reduc...

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Bibliographic Details
Main Authors: Castillo, F.A., Sweeney, J.D., Zirk, W.E.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Chemical industry
Computer science
control theory
systems
Evolutionary computation
Exact sciences and technology
Fitting
Genetic programming
Industrial relations
Input variables
Linear regression
Polynomials
Testing
US Department of Energy
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Description
Summary: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.
DOI:10.1109/CEC.2004.1330906