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A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building

  • Flor Castillo
  • Kenric Marshall
  • James Green
  • Arthur Kordon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2724)

Abstract

A novel methodology for empirical model building using GP-generated symbolic regression in combination with statistical design of experiments as well as undesigned data is proposed. The main advantage of this methodology is the maximum data utilization when extrapolation is necessary. The methodology offers alternative non-linear models that can either linearize the response in the presence of Lack or Fit or challenge and confirm the results from the linear regression in a cost effective and time efficient fashion. The economic benefit is the reduced number of additional experiments in the presence of Lack of Fit.

Keywords

Linear Regression Model Symbolic Regression Genetic Programming Model Genetic Programming Algorithm Transformed Linear Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Flor Castillo
    • 1
  • Kenric Marshall
    • 1
  • James Green
    • 1
  • Arthur Kordon
    • 1
  1. 1.The Dow Chemical CompanyFreeportUSA

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