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. 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)
  3. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  4. Chen, Xi; Zhou, Qiang: Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation (2017)
  5. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  6. Davins-Valldaura, Joan; Moussaoui, Saïd; Pita-Gil, Guillermo; Plestan, Franck: ParEGO extensions for multi-objective optimization of expensive evaluation functions (2017)
  7. Edwards, James; Fearnhead, Paul; Glazebrook, Kevin: On the identification and mitigation of weaknesses in the knowledge gradient policy for multi-armed bandits (2017)
  8. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  9. Hu, Ruimeng; Ludkovsk, Mike: Sequential design for ranking response surfaces (2017)
  10. 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)
  11. 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)
  12. Lombardi, Michele; Milano, Michela; Bartolini, Andrea: Empirical decision model learning (2017)
  13. Maatouk, Hassan; Bay, Xavier: Gaussian process emulators for computer experiments with inequality constraints (2017)
  14. Martinez, Nadia; Anahideh, Hadis; Rosenberger, Jay M.; Martinez, Diana; Chen, Victoria C.P.; Wang, Bo Ping: Global optimization of non-convex piecewise linear regression splines (2017)
  15. Müller, Juliane; Woodbury, Joshua D.: GOSAC: global optimization with surrogate approximation of constraints (2017)
  16. Namura, Nobuo; Shimoyama, Koji; Obayashi, Shigeru: Kriging surrogate model with coordinate transformation based on likelihood and gradient (2017)
  17. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  18. Scardua, Leonardo Azevedo; da Cruz, José Jaime: Complete offline tuning of the unscented Kalman filter (2017)
  19. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
  20. Steponavičė, Ingrida; Hyndman, Rob J.; Smith-Miles, Kate; Villanova, Laura: Dynamic algorithm selection for Pareto optimal set approximation (2017)

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