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 33 articles , 1 standard article )

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  1. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  2. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  3. Sanson, Francois; Le Maitre, Olivier; Congedo, Pietro Marco: Systems of Gaussian process models for directed chains of solvers (2019)
  4. Zhou, Yicheng; Lu, Zhenzhou; Cheng, Kai; Ling, Chunyan: An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework (2019)
  5. 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)
  6. ur Rehman, Samee; Langelaar, Matthijs: Expected improvement based infill sampling for global robust optimization of constrained problems (2017)
  7. Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
  8. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: A new expected-improvement algorithm for continuous minimax optimization (2016)
  9. Svenson, Joshua; Santner, Thomas: Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models (2016)
  10. 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)
  11. Wang, Shujuan; Li, Qiuyang; Savage, Gordon J.: Reliability-based robust design optimization of structures considering uncertainty in design variables (2015)
  12. Roy, Soma; Notz, William I.: Estimating percentiles in computer experiments: a comparison of sequential-adaptive designs and fixed designs (2014)
  13. Sóbester, András; Forrester, Alexander I. J.; Toal, David J. J.; Tresidder, Es; Tucker, Simon: Engineering design applications of surrogate-assisted optimization techniques (2014)
  14. Teytaud, Olivier; Vazquez, Emmanuel: Designing an optimal search algorithm with respect to prior information (2014)
  15. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: Worst-case global optimization of black-box functions through Kriging and relaxation (2013)
  16. Morio, Jérôme; Jacquemart, Damien; Balesdent, Mathieu; Marzat, Julien: Optimisation of interacting particle systems for rare event estimation (2013)
  17. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  18. Rullière, Didier; Faleh, Alaeddine; Planchet, Frédéric; Youssef, Wassim: Exploring or reducing noise? A global optimization algorithm in the presence of noise (2013)
  19. Yarotsky, Dmitry: Examples of inconsistency in optimization by expected improvement (2013)
  20. Yun, Yeboon; Nakayama, Hirotaka: Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms (2013)

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