A methodology for combining symbolic regression and design of experiments to improve empirical model building

Authors:
Flor Castillo;Kenric Marshall;James Green;Arthur Kordon
Affiliations:
The Dow Chemical Company, Freeport, TX;The Dow Chemical Company, Freeport, TX;The Dow Chemical Company, Freeport, TX;The Dow Chemical Company, Freeport, TX
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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Abstract

A novel methodology for empirical model building using GPgenerated 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.