Review of genetic programming in modeling of machining processes

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "A. Garg and K. Tai",
  title =        "Review of genetic programming in modeling of machining
  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 =          "",
  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