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 282 articles , 1 standard article )

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  1. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  2. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  3. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)
  4. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  5. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  6. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  7. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  8. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  9. Yang, Xiu; Zhu, Xueyu; Li, Jing: When bifidelity meets cokriging: an efficient physics-informed multifidelity method (2020)
  10. Barac, Diana; Multerer, Michael D.; Iber, Dagmar: Global optimization using Gaussian processes to estimate biological parameters from image data (2019)
  11. Bect, Julien; Bachoc, François; Ginsbourger, David: A supermartingale approach to Gaussian process based sequential design of experiments (2019)
  12. Chen, Ye; Ryzhov, Ilya O.: Complete expected improvement converges to an optimal budget allocation (2019)
  13. Fuhg, Jan N.; Fau, Amélie: Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type (2019)
  14. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)
  15. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  16. Letham, Benjamin; Karrer, Brian; Ottoni, Guilherme; Bakshy, Eytan: Constrained Bayesian optimization with noisy experiments (2019)
  17. Mohammadi, Hossein; Challenor, Peter; Goodfellow, Marc: Emulating dynamic non-linear simulators using Gaussian processes (2019)
  18. Nachar, Stéphane; Boucard, Pierre-Alain; Néron, David; Bordeu, Felipe: Coupling multi-fidelity Kriging and model-order reduction for the construction of virtual charts (2019)
  19. Naik, Pratik; Pandita, Piyush; Aramideh, Soroush; Bilionis, Ilias; Ardekani, Arezoo M.: Bayesian model calibration and optimization of surfactant-polymer flooding (2019)
  20. Peherstorfer, Benjamin: Multifidelity Monte Carlo estimation with adaptive low-fidelity models (2019)

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