A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building
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.
KeywordsLinear Regression Model Symbolic Regression Genetic Programming Model Genetic Programming Algorithm Transformed Linear Model
Unable to display preview. Download preview PDF.
- 3.Castillo F. A., Marshall, K. A, Green, J.L., and Kordon, A, “Symbolic Regression in Design of Experiments: A Case Study with Linearizing Transformations, Proceedings of GECCO’2002, New York, pp. 1043–1048., 2002Google Scholar
- 5.Myers, R.H., and, Montgomery, D.C., Response Surface Methodology, John Wiley and Sons, New York, 1995Google Scholar