space (Stochastic Process Analysis of Computer Experiments): The code is useful for analysis and global optimization of very expensive functions. The major functions are : Fitting or Estimation of the stochastic process model parameters Cross validation of the model fit Prediction at new design sites (x-values) using the fitted model Visualization of Main Effects and Joint Effects Global minimization of the response in stages: The code suggests a specified number of design sites at each stage. The function can then be evaluated off - line at these design sites. The new function evaluations are fed back to space for the next stage. Global minimization with supplied function of the response. space generates a single design site, waits for the design site to be evaluated by a supplied function, space generates the next design site given the new function evaluation , etc., until a convergence criterion is satisfied.

References in zbMATH (referenced in 23 articles , 1 standard article )

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  1. Li, Yaohui; Wu, Yizhong; Zhao, Jianjun; Chen, Liping: A Kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points (2017)
  2. Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
  3. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: A new expected-improvement algorithm for continuous minimax optimization (2016)
  4. Feng, Zhiwei; Zhang, Qingbin; Zhang, Qingfu; Tang, Qiangang; Yang, Tao; Ma, Yang: A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization (2015)
  5. Sóbester, András; Forrester, Alexander I.J.; Toal, David J.J.; Tresidder, Es; Tucker, Simon: Engineering design applications of surrogate-assisted optimization techniques (2014)
  6. Teytaud, Olivier; Vazquez, Emmanuel: Designing an optimal search algorithm with respect to prior information (2014)
  7. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: Worst-case global optimization of black-box functions through Kriging and relaxation (2013)
  8. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  9. Rullière, Didier; Faleh, Alaeddine; Planchet Frédéric; Youssef, Wassim: Exploring or reducing noise? A global optimization algorithm in the presence of noise (2013)
  10. Yarotsky, Dmitry: Examples of inconsistency in optimization by expected improvement (2013)
  11. Yun, Yeboon; Nakayama, Hirotaka: Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms (2013)
  12. Basudhar, Anirban; Dribusch, Christoph; Lacaze, Sylvain; Missoum, Samy: Constrained efficient global optimization with support vector machines (2012)
  13. Kleijnen, Jack P.C.; van Beers, Wim; van Nieuwenhuyse, Inneke: Expected improvement in efficient global optimization through bootstrapped Kriging (2012)
  14. Lizotte, Daniel J.; Greiner, Russell; Schuurmans, Dale: An experimental methodology for response surface optimization methods (2012)
  15. Wiebenga, J.H.; Den Boogaard, A.H.Van; Klaseboer, G.: Sequential robust optimization of a V-bending process using numerical simulations (2012)
  16. Bonte, Martijn H.A.; Fourment, Lionel; Do, Tien-Tho; Den Boogaard, A.H.Van; Huétink, J.: Optimization of forging processes using finite element simulations (2010)
  17. Vazquez, Emmanuel; Bect, Julien: Convergence properties of the expected improvement algorithm with fixed mean and covariance functions (2010)
  18. Villemonteix, Julien; Vazquez, Emmanuel; Walter, Eric: An informational approach to the global optimization of expensive-to-evaluate functions (2009)
  19. Morgans, Rick C.; Zander, Anthony C.; Hansen, Colin H.; Murphy, David J.: EGO shape optimization of Horn-loaded loudspeakers (2008)
  20. Turner, Cameron J.; Crawford, Richard H.; Campbell, Matthew I.: Multidimensional sequential sampling for nURBs-based metamodel development (2007)

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