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