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 214 articles , 1 standard article )

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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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