Appraisal of Surrogate Modeling Techniques: A Case Study of Electromagnetic Device

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@Article{Mendes:2013:IEEEMagnetics,
  author =       "Marcus H. S. Mendes and Gustavo L. Soares and 
                 Jean-Louis Coulomb and Joao A. Vasconcelos",
  journal =      "IEEE Transactions on Magnetics",
  title =        "Appraisal of Surrogate Modeling Techniques: A Case
                 Study of Electromagnetic Device",
  year =         "2013",
  volume =       "49",
  number =       "5",
  pages =        "1993--1996",
  size =         "4 pages",
  keywords =     "genetic algorithms, genetic programming, Interval
                 robust optimisation, TEAM 22 problem, surrogate
                 modelling",
  DOI =          "doi:10.1109/TMAG.2013.2241401",
  ISSN =         "0018-9464",
  abstract =     "Simulations are successfully used to reproduce the
                 behaviour of complex systems in many knowledge fields.
                 The computational effort is a key factor when high-cost
                 simulations are required in optimisation, principally,
                 if the system to be optimised operates under uncertain
                 conditions. In this context, surrogate modelling is
                 useful to alleviate the CPU time. Hence, this paper
                 presents a methodology to assess three surrogate
                 techniques based on genetic programming (GP), a radial
                 basis function neural network (RBF-NNs), and universal
                 Kriging. These techniques are used in this paper to
                 obtain analytical optimisation functions that are
                 accurate, fast to evaluate and suitable for interval
                 robust optimisation. The experiments were performed in
                 a robust version of the TEAM 22 problem. The results
                 show that the surrogate models obtained are reliable
                 and appropriate for interval robust methods. The
                 methodology presented is flexible and extensible to
                 other problems in diverse fields of interest.",
  notes =        "Also known as \cite{6514603}",
}

Genetic Programming entries for Marcus H S Mendes Gustavo L Soares Jean-Louis Coulomb Joao Antonio de Vasconcelos

Citations