Symbolic regression in multicollinearity problems

Authors:
Flor A. Castillo;Carlos M. Villa
Affiliations:
The Dow Chemical Company, Freeport, TX;The Dow Chemical Company, Freeport, TX
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 2
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments

Response Surface Methodology: Process and Product in Optimization Using Designed Experiments

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper the potential of GP-generated symbolic regression for alleviating multicollinearity problems in multiple regression is presented with a case study in an industrial setting. The main advantage of this approach is the potential to produce a simple and stable polynomial model in terms of the original variables.