Review of genetic programming in modeling of machining processes
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Garg:2012:ICMIC,
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author = "A. Garg and K. Tai",
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title = "Review of genetic programming in modeling of machining
processes",
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booktitle = "Proceedings of International Conference on Modelling,
Identification Control (ICMIC 2012)",
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year = "2012",
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month = "24-26 " # jun,
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pages = "653--658",
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address = "Wuhan, China",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, ANN",
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isbn13 = "978-1-4673-1524-1",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260225",
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size = "6 pages",
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abstract = "The mathematical modelling of machining processes has
received immense attention and attracted a number of
researchers because of its significant contribution to
the overall cost and quality of product. The literature
study demonstrates that conventional approaches such as
statistical regression, response surface methodology,
etc. requires physical understanding of the process for
the erection of precise and accurate models. The
statistical assumptions of such models induce ambiguity
in the prediction ability of the model. Such
limitations do not prevail in the nonconventional
modelling approaches such as Genetic Programming (GP),
Artificial Neural Network (ANN), Fuzzy Logic (FL),
Genetic Algorithm (GA), etc. and therefore ensures
trustworthiness in the prediction ability of the model.
The present work discusses about the notion,
application, abilities and limitations of Genetic
Programming for modelling of machining processes. The
characteristics of GP uncovered from the current review
are compared with features of other modelling
approaches applied to machining processes.",
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notes = "Also known as \cite{6260225}",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
Citations