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Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models


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  • D. Huang


  • T. Allen


  • W. Notz


  • N. Zeng


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    No abstract is available for this item.

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    Bibliographic Info

    Article provided by Springer in its journal Journal of Global Optimization.

    Volume (Year): 34 (2006)
    Issue (Month): 3 (03)
    Pages: 441-466

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    Handle: RePEc:spr:jglopt:v:34:y:2006:i:3:p:441-466

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    Keywords: Efficient global optimization; expected improvement; kriging; stochastic black-box systems;


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    1. Angun, M.E. & Gürkan, G. & Hertog, D. den & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Open Access publications from Tilburg University urn:nbn:nl:ui:12-91399, Tilburg University.
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    Cited by:
    1. Janis Janusevskis & Rodolphe Le Riche, 2013. "Simultaneous kriging-based estimation and optimization of mean response," Journal of Global Optimization, Springer, vol. 55(2), pages 313-336, February.
    2. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
    3. Rivera-Gómez, Héctor & Gharbi, Ali & Kenné, Jean Pierre, 2013. "Joint production and major maintenance planning policy of a manufacturing system with deteriorating quality," International Journal of Production Economics, Elsevier, vol. 146(2), pages 575-587.
    4. Kleijnen, Jack P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2010. "Constrained optimization in simulation: A novel approach," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3583585, Tilburg University.
    5. Dellino, G. & Lino, P. & Meloni, C. & Rizzo, A., 2009. "Kriging metamodel management in the design optimization of a CNG injection system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2345-2360.
    6. Rommel Regis & Christine Shoemaker, 2013. "A quasi-multistart framework for global optimization of expensive functions using response surface models," Journal of Global Optimization, Springer, vol. 56(4), pages 1719-1753, August.
    7. Daniel Lizotte & Russell Greiner & Dale Schuurmans, 2012. "An experimental methodology for response surface optimization methods," Journal of Global Optimization, Springer, vol. 53(4), pages 699-736, August.
    8. Nestor Queipo & Salvador Pintos & Efrain Nava, 2013. "Setting targets for surrogate-based optimization," Journal of Global Optimization, Springer, vol. 55(4), pages 857-875, April.
    9. Tansu Alpcan, 2013. "A framework for optimization under limited information," Journal of Global Optimization, Springer, vol. 55(3), pages 681-706, March.
    10. Felipe Viana & Raphael Haftka & Layne Watson, 2013. "Efficient global optimization algorithm assisted by multiple surrogate techniques," Journal of Global Optimization, Springer, vol. 56(2), pages 669-689, June.
    11. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.


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