# Nonlinear regression with genetic programming for data from full-factorial design

I am trying to do nonlinear regression on a dataset obtained from full factorial design (2 and 3 levels) with the help of genetic programming.

Is there something fundamentally wrong with trying to construct a nonlinear regression model with full-factorial design? My background is not in statistics, and I did read somewhere that full factorial design (esp when few levels are used) are supposed to be used at most to identify contribution of interaction factors, and key factors, and not for full blown nonlinear regression.

 What do you mean under nonlinear regression ... with the help of genetic programming? Could you provide more details? Why don't you simply use polynimial fitting? – Paul Jul 17 '12 at 7:03 genetic programming is able to find a mathematical model of data by the evolutionary computing method of iterative test-generate-select. given a set of operators, and training data (input-output), the program constructs randomly a set of expression trees, refined through many iterations of recombination , mutation, etc, (which in effect is exchange of subtrees), fit evaluation, and fitness based selection to arrive at a model of data. i hope that's somewhat descriptive. i tried regression trees, polynomials, and the result on training and test data is not as good as genetic programming. – oalah Jul 17 '12 at 11:29