The Efficient Global Optimization (EGO) algorithm solves costly box-bounded global optimization problems with additional linear, nonlinear and integer constraints. The idea of the EGO algorithm is to first fit a response surface to data collected by evaluating the objective function at a few points. Then, EGO balances between finding the minimum of the surface and improving the approximation by sampling where the prediction error may be high.

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  1. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  2. Kostorz, Wawrzyniec: A practical method for well log data classification (2021)
  3. Mathesen, Logan; Pedrielli, Giulia; Ng, Szu Hui; Zabinsky, Zelda B.: Stochastic optimization with adaptive restart: a framework for integrated local and global learning (2021)
  4. Peng, Yijie; Chen, Chun-Hung; Fu, Michael C.; Hu, Jian-Qiang; Ryzhov, Ilya O.: Efficient sampling allocation procedures for optimal quantile selection (2021)
  5. Sabater, Christian; Le Maître, Olivier; Congedo, Pietro Marco; Görtz, Stefan: A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources (2021)
  6. Sinsbeck, Michael; Cooke, Emily; Nowak, Wolfgang: Sequential design of computer experiments for the computation of Bayesian model evidence (2021)
  7. Wang, Hu; Hu, Wei; Li, Enying: Handling of constraints in efficient global optimization (2021)
  8. Zhang, Xinshuai; Xie, Fangfang; Ji, Tingwei; Zhu, Zaoxu; Zheng, Yao: Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (2021)
  9. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  10. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  11. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  12. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)
  13. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  14. Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)
  15. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  16. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  17. Broto, Baptiste; Bachoc, François; Depecker, Marine: Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution (2020)
  18. Candelieri, Antonio; Perego, Riccardo; Giordani, Ilaria; Archetti, Francesco: Are humans Bayesian in the optimization of black-box functions? (2020)
  19. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  20. Chen, Ray-Bing; Wang, Yuan; Wu, C. F. Jeff: Finding optimal points for expensive functions using adaptive RBF-based surrogate model via uncertainty quantification (2020)

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