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. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  2. Damblin, Guillaume; Barbillon, Pierre; Keller, Merlin; Pasanisi, Alberto; Parent, Éric: Adaptive numerical designs for the calibration of computer codes (2018)
  3. Wang, Yingfei; Powell, Warren B.: Finite-time analysis for the knowledge-gradient policy (2018)
  4. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  5. Andrianakis, Ioannis; McCreesh, Nicky; Vernon, Ian; McKinley, Trevelyan J.; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.: Efficient history matching of a high dimensional individual-based HIV transmission model (2017)
  6. Barbillon, Pierre; Barthélémy, Célia; Samson, Adeline: Parameter estimation of complex mixed models based on meta-model approach (2017)
  7. Ben Salem, Malek; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Universal prediction distribution for surrogate models (2017)
  8. Boukouvala, Fani; Faruque Hasan, M.M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)
  9. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  10. Chen, Xi; Zhou, Qiang: Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation (2017)
  11. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  12. Davins-Valldaura, Joan; Moussaoui, Saïd; Pita-Gil, Guillermo; Plestan, Franck: ParEGO extensions for multi-objective optimization of expensive evaluation functions (2017)
  13. Edwards, James; Fearnhead, Paul; Glazebrook, Kevin: On the identification and mitigation of weaknesses in the knowledge gradient policy for multi-armed bandits (2017)
  14. Fajraoui, Noura; Marelli, Stefano; Sudret, Bruno: Sequential design of experiment for sparse polynomial chaos expansions (2017)
  15. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  16. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  17. Hu, Ruimeng; Ludkovsk, Mike: Sequential design for ranking response surfaces (2017)
  18. Jie, Haoxiang; Wu, Yizhong; Zhao, Jianjun; Ding, Jianwan; Liangliang: An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems (2017)
  19. 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)
  20. Lombardi, Michele; Milano, Michela; Bartolini, Andrea: Empirical decision model learning (2017)

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