Symbolic and numerical regression: experiments and applications

This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the C...

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Veröffentlicht in:Information sciences Jg. 150; H. 1; S. 95 - 117
Hauptverfasser: Davidson, J.W., Savic, D.A., Walters, G.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.03.2003
Schlagworte:
Genetic programming
Least squares
Rule-based programming
Stepwise regression
Symbolic regression
Symbolic regression
Rule-based programming
Stepwise regression
Genetic programming
Least squares
ISSN:0020-0255, 1872-6291
Online-Zugang:Volltext
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Zusammenfassung:This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook–White equation and rainfall-runoff modelling. The three example problems illustrate the advantages of the new method.
ISSN:0020-0255
1872-6291
DOI:10.1016/S0020-0255(02)00371-7