Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Omkar:2009:IJEMS,
  title =        "Mathematical model and rule extraction for tool wear
                 monitoring problem using nature inspired techniques",
  author =       "S. N. Omkar and J Senthilnath and S. Suresh",
  journal =      "Indian Journal of Engineering \& Materials Sciences",
  year =         "2009",
  volume =       "16",
  number =       "4",
  pages =        "205--210",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Tool wear
                 monitoring, Ant-Miner",
  publisher =    "National Institute of Science Communication and
                 Information Resources",
  bibsource =    "OAI-PMH server at eprints.iisc.ernet.in",
  oai =          "oai:generic.eprints.org:25161",
  type =         "Peer Reviewed",
  URL =          "http://eprints.iisc.ernet.in/25161/1/IJEMS\%2016(4)\%20205-210.pdf",
  URL =          "http://apps.isiknowledge.com/full_record.do?product=WOS\&search_mode=GeneralSearch\&qid=19\&SID=Z139CFFemL1l58elcdo\&page=1\&doc=1",
  URL =          "http://eprints.iisc.ernet.in/25161/",
  ISSN =         "0971-4588",
  abstract =     "In this paper, pattern classification problem in tool
                 wear monitoring is solved using nature inspired
                 techniques such as Genetic Programming (GP) and
                 Ant-Miner (AM). The main advantage of GP and AM is
                 their ability to learn the underlying data
                 relationships and express them in the form of
                 mathematical equation or simple rules. The extraction
                 of knowledge from the training data set using GP and AM
                 are in the form of Genetic Programming Classifier
                 Expression (GPCE) and rules respectively. The GPCE and
                 AM extracted rules are then applied to set of data in
                 the testing/validation set to obtain the classification
                 accuracy. A major attraction in GP evolved GPCE and AM
                 based classification is the possibility of obtaining an
                 expert system like rules that can be directly applied
                 subsequently by the user in his/her application. The
                 performance of the data classification using GP and AM
                 is as good as the classification accuracy obtained in
                 the earlier study (i.e. using ANN approach).",
}

Genetic Programming entries for S N Omkar J Senthilnath Sundaram Suresh

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