Multivarible Symbolic Regression Based on Gene Expression Programming

Ming-fang Zhu, Jian-bin Zhang, Yan-ling Ren, Yu Pan, Guanghui Zhu
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引用次数: 1

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 statistiacal linear regression techniques.
基于基因表达式编程的多变量符号回归
本文提出了一种多变量符号回归建模与预测方法。该方法基于最近提出的一种进化计算技术——基因表达式编程。详细介绍了基因表达式编程和多变量符号回归技术。实验结果表明,利用基因表达式编程建立的模型优于统计线性回归技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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