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 200 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. 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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