Multivarible Symbolic Regression Based on Gene Expression Programming

Author

Zhu, Ming-fang ; Zhang, Jian-bin ; Ren, Yan-ling ; Pan, Yu ; Zhu, Guang-ping

Author_Institution

Sch. of Comput. Eng., Jiangsu Teachers Univ. of Technol., Changzhou, China

Volume

2

fYear

2011

fDate

28-30 Oct. 2011

Firstpage

298

Lastpage

301

Abstract

This paper presents a method for multivarible symbolic regression modeling and predicting. The method based on gene expression programming, a recently proposed evolutionary computation technique. We explain in details the techniques of gene expression programming and multivarible symbolic regression with gene expression programming. Furthermore, we give an example to explain this technique, and experiment results show that the model set up by gene expression programming is better than statisticacal linear regression techniques.

Keywords

evolutionary computation; regression analysis; evolutionary computation technique; gene expression programming; multivarible symbolic regression modeling; statistiacal linear regression techniques; Biological cells; Computational modeling; Data models; Educational institutions; Gene expression; Programming; autimatic modeling; evolutionary computation; gene expression programming; multiable symbolic regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4577-1085-8

Type

conf

DOI

10.1109/ISCID.2011.177

Filename

6079796