Multivarible Symbolic Regression Based on Gene Expression Programming

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@InProceedings{Zhu:2011:ISCID,

• author = "Ming-fang Zhu and Jian-bin Zhang and Yan-ling Ren and Yu Pan and Guang-ping Zhu",
• title = "Multivarible Symbolic Regression Based on Gene Expression Programming",
• booktitle = "Fourth International Symposium on Computational Intelligence and Design (ISCID 2011)",
• year = "2011",
• month = "28-30 " # oct,
• address = "Hangzhou",
• publisher = "IEEE",
• DOI = "doi:10.1109/ISCID.2011.177",
• volume = "2",
• pages = "298--301",
• isbn13 = "978-1-4577-1085-8",
• size = "4 pages",
• abstract = "This paper presents a method for multivarible symbolic regression modelling 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 statistical linear regression techniques.",
• keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary computation technique, multivarible symbolic regression modelling, statistical linear regression techniques, evolutionary computation, regression analysis",
• notes = "Also known as \cite{6079796}",

}

Genetic Programming entries for Mingfang Zhu Jian-bin Zhang Yan-ling Ren Yu Pan Guang-ping Zhu

Citations