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 349 articles , 1 standard article )

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  1. Kostorz, Wawrzyniec: A practical method for well log data classification (2021)
  2. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  3. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  4. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  5. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)
  6. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  7. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  8. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  9. Broto, Baptiste; Bachoc, François; Depecker, Marine: Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution (2020)
  10. Candelieri, Antonio; Perego, Riccardo; Giordani, Ilaria; Archetti, Francesco: Are humans Bayesian in the optimization of black-box functions? (2020)
  11. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  12. 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)
  13. García-García, José Carlos; García-Ródenas, Ricardo; Codina, Esteve: A surrogate-based cooperative optimization framework for computationally expensive black-box problems (2020)
  14. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  15. Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar: Bayesian nonparametric policy search with application to periodontal recall intervals (2020)
  16. Han, Weidong; Powell, Warren B.: Optimal online learning for nonlinear belief models using discrete priors (2020)
  17. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  18. Kontogiannis, Spyridon G.; Savill, Mark A.: A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis (2020)
  19. Ling, Chunyan; Lu, Zhenzhou; Sun, Bo; Wang, Minjie: An efficient method combining active learning kriging and Monte Carlo simulation for profust failure probability (2020)
  20. Luo, Yangjun; Xing, Jian; Kang, Zhan: Topology optimization using material-field series expansion and Kriging-based algorithm: an effective non-gradient method (2020)

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