Symbolic regression in design of experiments: a case study with linearizing transformations
Flor Alba Castillo, Ken A. Marshall, James L. Green, Arthur K. Kordon · 2002
The paper presents the potential of genetic programming (GP)-generated symbolic regression for linearizing the response in statistical design of experiments when significant Lack of Fit is detected and no additional experimental runs are economically or technically feasible because of extreme experimental conditions. An application of this approach is presented with a case study in an industrial setting at The Dow Chemical Company. 1