EGO

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.


References in zbMATH (referenced in 363 articles , 1 standard article )

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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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