Multivarible Symbolic Regression Based on Gene Expression Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Zhu:2011:ISCID,
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author = "Ming-fang Zhu and Jian-bin Zhang and Yan-ling Ren and
Yu Pan and Guang-ping Zhu",
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title = "Multivarible Symbolic Regression Based on Gene
Expression Programming",
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booktitle = "Fourth International Symposium on Computational
Intelligence and Design (ISCID 2011)",
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year = "2011",
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month = "28-30 " # oct,
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address = "Hangzhou",
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publisher = "IEEE",
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DOI = "doi:10.1109/ISCID.2011.177",
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volume = "2",
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pages = "298--301",
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isbn13 = "978-1-4577-1085-8",
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size = "4 pages",
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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.",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, evolutionary computation
technique, multivarible symbolic regression modelling,
statistical linear regression techniques, evolutionary
computation, regression analysis",
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notes = "Also known as \cite{6079796}",
- }
Genetic Programming entries for
Mingfang Zhu
Jian-bin Zhang
Yan-ling Ren
Yu Pan
Guang-ping Zhu
Citations