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,
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author = "Marcus H. S. Mendes and Gustavo L. Soares and
Jean-Louis Coulomb and Joao A. Vasconcelos",
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title = "A Surrogate Genetic Programming Based Model to
Facilitate Robust Multi-Objective Optimization: A Case
Study in Magnetostatics",
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journal = "IEEE Transactions on Magnetics",
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year = "2013",
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month = may,
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volume = "49",
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number = "5",
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pages = "2065--2068",
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keywords = "genetic algorithms, genetic programming, Finite
element method, TEAM 22 problem, robust optimisation,
surrogate model",
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DOI = "doi:10.1109/TMAG.2013.2238615",
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ISSN = "0018-9464",
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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.",
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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