Multivarible Symbolic Regression Based on Gene Expression Programming

摘要：

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 statistiacal linear regression techniques.

关键词： gene expression programming multiable symbolic regression autimatic modeling evolutionary computation

作者: Ming-fang Zhu Jian-bin Zhang Yan-ling Ren Yu Pan Guang-ping Zhu

作者单位: School of Computer Engineering, Jiangsu Teachers University of Technology, Changzhou, China School of Electronic Infomatuion, Jiangsu Teachers University of Technology, Changzhou, China

会议类型: 国际会议

会议名称: 2011 Fourth International Symposium on Computational Interlligence and Design 第四届计算智能与设计国际会议 ISCID 2011

会议地点: 杭州

会议语种:英文

页码: 678-681

在线出版日期: 2011-10-28（万方平台首次上网日期，不代表论文的发表时间）