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