A Surrogate Genetic Programming Based Model to Facilitate Robust Multi-Objective Optimization: A Case Study in Magnetostatics

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@Article{Mendes:2013:IEEEMagnetics2,
  author =       "Marcus H. S. Mendes and Gustavo L. Soares and 
                 Jean-Louis Coulomb and Joao A. Vasconcelos",
  title =        "A Surrogate Genetic Programming Based Model to
                 Facilitate Robust Multi-Objective Optimization: A Case
                 Study in Magnetostatics",
  journal =      "IEEE Transactions on Magnetics",
  year =         "2013",
  month =        may,
  volume =       "49",
  number =       "5",
  pages =        "2065--2068",
  keywords =     "genetic algorithms, genetic programming, Finite
                 element method, TEAM 22 problem, robust optimisation,
                 surrogate model",
  DOI =          "doi:10.1109/TMAG.2013.2238615",
  ISSN =         "0018-9464",
  abstract =     "A common drawback of robust optimisation methods is
                 the effort expended to compute the influence of
                 uncertainties, because the objective and constraint
                 functions must be re-evaluated many times. This
                 disadvantage can be aggravated if time-consuming
                 methods, such as boundary or finite element methods are
                 required to calculate the optimisation functions. To
                 overcome this difficulty, we propose the use of genetic
                 programming to obtain high-quality surrogate functions
                 that are quickly evaluated. Such functions can be used
                 to compute the values of the optimisation functions in
                 place of the burdensome methods. The proposal has been
                 tested on a version of the TEAM 22 benchmark problem
                 with uncertainties in decision parameters. The
                 performance of the methodology has been compared with
                 results in the literature, ensuring its suitability,
                 significant CPU time savings and substantial reduction
                 in the number of computational simulations.",
  notes =        "Also known as \cite{6514790}",
}

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

Citations