Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions

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

@InProceedings{conf/semcco/GargT13,
  author =       "Akhil Garg and Kang Tai",
  title =        "Genetic Programming for Modeling Vibratory Finishing
                 Process: Role of Experimental Designs and Fitness
                 Functions",
  booktitle =    "Proceedings of the 4th International Conference on
                 Swarm, Evolutionary, and Memetic Computing (SEMCCO
                 2013), Part II",
  year =         "2013",
  editor =       "Bijaya Ketan Panigrahi and 
                 Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Subhransu Sekhar Dash",
  volume =       "8298",
  series =       "Lecture Notes in Computer Science",
  pages =        "23--31",
  address =      "Chennai, India",
  month =        dec # " 19-21",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, vibratory
                 finishing, fitness function, vibratory modelling,
                 GPTIPS, experimental designs, finishing modelling",
  isbn13 =       "978-3-319-03755-4",
  bibdate =      "2013-12-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/semcco/semcco2013-2.html#GargT13",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03756-1",
  DOI =          "doi:10.1007/978-3-319-03756-1_3",
  abstract =     "Manufacturers seek to improve efficiency of vibratory
                 finishing process while meeting increasingly stringent
                 cost and product requirements. To serve this purpose,
                 mathematical models have been formulated using soft
                 computing methods such as artificial neural network and
                 genetic programming (GP). Among these methods, GP
                 evolves model structure and its coefficients
                 automatically. There is extensive literature on ways to
                 improve the performance of GP but less attention has
                 been paid to the selection of appropriate experimental
                 designs and fitness functions. The evolution of fitter
                 models depends on the experimental design used to
                 sample the problem (system) domain, as well as on the
                 appropriate fitness function used for improving the
                 evolutionary search. This paper presents quantitative
                 analysis of two experimental designs and four fitness
                 functions used in GP for the modelling of vibratory
                 finishing process. The results conclude that fitness
                 function SRM and PRESS evolves GP models of higher
                 generalisation ability, which may then be deployed by
                 experts for optimisation of the finishing process.",
}

Genetic Programming entries for Akhil Garg Kang Tai

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