Using evolutionary algorithms to suggest variable transformations in linear model lack-of-fit situations

被引：3

作者：

Castillo, FA ^{[1
]}

Sweeney, JD ^{[1
]}

Zirk, WE ^{[1
]}

机构：

[1] Dow Chem Co USA, Freeport, TX 77541 USA

来源：

CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2004年

关键词：

D O I：

10.1109/CEC.2004.1330906

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

When significant model lack of fit (LOF) is present in a second-order linear regression model, it is often difficult to propose the appropriate parameter transformation that will make model LOF insignificant. This paper presents the potential of genetic programming (GP) symbolic regression for reducing or eliminating significant second-order linear model LOF. A case study in an industrial setting at The Dow Chemical Company will be presented to illustrate this methodology.

引用

页码：556 / 560

页数：5