Review of genetic programming in modeling of machining processes

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Garg:2012:ICMIC,

• author = "A. Garg and K. Tai",
• title = "Review of genetic programming in modeling of machining processes",
• booktitle = "Proceedings of International Conference on Modelling, Identification Control (ICMIC 2012)",
• year = "2012",
• month = "24-26 " # jun,
• pages = "653--658",
• address = "Wuhan, China",
• keywords = "genetic algorithms, genetic programming, gene expression programming, ANN",
• isbn13 = "978-1-4673-1524-1",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260225",
• size = "6 pages",
• 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.",
• notes = "Also known as \cite{6260225}",

}

Genetic Programming entries for Akhil Garg Kang Tai

Citations