1. Multivarible Symbolic Regression Based on Gene Expression Programming
- Author
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Ming-fang Zhu, Jian-bin Zhang, Guang-ping Zhu, Yan-ling Ren, and Yu Pan
- Subjects
ComputingMethodologies_PATTERNRECOGNITION ,Theoretical computer science ,Computer science ,Linear regression ,Model set ,Regression analysis ,Gene expression programming ,Symbolic regression ,Evolutionary computation ,Data modeling - 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.
- Published
- 2011
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