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 37 articles , 1 standard article )

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  1. Mohammadi, Hossein; Challenor, Peter; Williamson, Daniel; Goodfellow, Marc: Cross-validation-based adaptive sampling for Gaussian process models (2022)
  2. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  3. García-García, José Carlos; García-Ródenas, Ricardo; Codina, Esteve: A surrogate-based cooperative optimization framework for computationally expensive black-box problems (2020)
  4. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  5. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  6. Zhan, Dawei; Xing, Huanlai: Expected improvement for expensive optimization: a review (2020)
  7. Sanson, Francois; Le Maitre, Olivier; Congedo, Pietro Marco: Systems of Gaussian process models for directed chains of solvers (2019)
  8. 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)
  9. 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)
  10. ur Rehman, Samee; Langelaar, Matthijs: Expected improvement based infill sampling for global robust optimization of constrained problems (2017)
  11. Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
  12. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: A new expected-improvement algorithm for continuous minimax optimization (2016)
  13. Svenson, Joshua; Santner, Thomas: Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models (2016)
  14. 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)
  15. Wang, Shujuan; Li, Qiuyang; Savage, Gordon J.: Reliability-based robust design optimization of structures considering uncertainty in design variables (2015)
  16. Roy, Soma; Notz, William I.: Estimating percentiles in computer experiments: a comparison of sequential-adaptive designs and fixed designs (2014)
  17. Sóbester, András; Forrester, Alexander I. J.; Toal, David J. J.; Tresidder, Es; Tucker, Simon: Engineering design applications of surrogate-assisted optimization techniques (2014)
  18. Teytaud, Olivier; Vazquez, Emmanuel: Designing an optimal search algorithm with respect to prior information (2014)
  19. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: Worst-case global optimization of black-box functions through Kriging and relaxation (2013)
  20. Morio, Jérôme; Jacquemart, Damien; Balesdent, Mathieu; Marzat, Julien: Optimisation of interacting particle systems for rare event estimation (2013)

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