Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Garg:2013:SSCI,
  author =       "A. Garg and K. Tai",
  title =        "Selection of a robust experimental design for the
                 effective modeling of nonlinear systems using Genetic
                 Programming",
  booktitle =    "IEEE Symposium on Computational Intelligence and Data
                 Mining, CIDM 2013",
  year =         "2013",
  editor_ssci-2013 = "P. N. Suganthan",
  editor =       "Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and 
                 Nitesh Chawla",
  pages =        "287--292",
  address =      "Singapore",
  month =        "16-19 " # apr,
  keywords =     "genetic algorithms, genetic programming, experimental
                 designs, latin hypercube sampling, full factorial
                 design, response surface design",
  DOI =          "doi:10.1109/CIDM.2013.6597249",
  size =         "6 pages",
  abstract =     "The evolutionary approach of Genetic Programming (GP)
                 has been applied extensively to model various
                 non-linear systems. The distinct advantage of using GP
                 is that prior assumptions for the selection of a model
                 structure are not required. The GP automatically
                 evolves the optimal model structure and its parameters
                 that best describe the system characteristics. However,
                 the evolution of an optimal model structure is highly
                 dependent on the experimental designs used to sample
                 the problem (system) domain and capture its
                 characteristics. The literature reveals that very few
                 researchers have studied the effect of various
                 experimental designs on the performance of GP models
                 and therefore the optimum choice of an experimental
                 design is still unknown. This paper studies the effect
                 of various experimental designs on the performance of
                 GP models on two non-linear test functions. The
                 objective of the paper is to identify the most robust
                 (best) experimental design for effective modelling of
                 non-linear test functions using GP. The analysis
                 reveals that for the test function 1, the experimental
                 design that gives best performance of GP models is
                 response surface faced design and for test function 2,
                 the best experimental design is 5-level full factorial
                 design. Thus, the result concludes that the selection
                 of the robust experimental design is a crucial
                 preprocessing step for the effective modelling of
                 non-linear systems using GP.",
  notes =        "School of Mechanical and Aerospace Engineering Nanyang
                 Technological University Singapore

                 CIDM 2013,
                 http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/CIDM2013.htm
                 also known as \cite{6597249}",
}

Genetic Programming entries for Akhil Garg Kang Tai

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