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 156 articles , 1 standard article )

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  1. Akhtar, Taimoor; Shoemaker, Christine A.: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection (2016)
  2. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  3. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  4. Calandra, Roberto; Seyfarth, André; Peters, Jan; Deisenroth, Marc Peter: Bayesian optimization for learning gaits under uncertainty. An experimental comparison on a dynamic bipedal walker (2016)
  5. Courrier, Nicolas; Boucard, Pierre-Alain; Soulier, Bruno: Variable-fidelity modeling of structural analysis of assemblies (2016)
  6. Durantin, Cédric; Marzat, Julien; Balesdent, Mathieu: Analysis of multi-objective Kriging-based methods for constrained global optimization (2016)
  7. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)
  8. Jones, Matthew; Goldstein, Michael; Jonathan, Philip; Randell, David: Bayes linear analysis for Bayesian optimal experimental design (2016)
  9. Karademir, Serdar; Prokopyev, Oleg A.; Mailloux, Robert J.: Irregular polyomino tiling via integer programming with application in phased array antenna design (2016)
  10. Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.: SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems (2016)
  11. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  12. Marzat, Julien; Walter, Eric; Piet-Lahanier, Hélène: A new expected-improvement algorithm for continuous minimax optimization (2016)
  13. Xie, Jing; Frazier, Peter I.; Chick, Stephen E.: Bayesian optimization via simulation with pairwise sampling and correlated prior beliefs (2016)
  14. Cheng, Bolong; Jamshidi, Arta; Powell, Warren B.: Optimal learning with a local parametric belief model (2015)
  15. Defourny, Boris; Ryzhov, Ilya O.; Powell, Warren B.: Optimal information blending with measurements in the $L^2$ sphere (2015)
  16. Feng, Zhiwei; Zhang, Qingbin; Zhang, Qingfu; Tang, Qiangang; Yang, Tao; Ma, Yang: A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization (2015)
  17. Gramacy, Robert B.; Ludkovski, Michael: Sequential design for optimal stopping problems (2015)
  18. Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe: A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry (2015)
  19. Liu, Haitao; Xu, Shengli; Ma, Ying; Wang, Xiaofang: Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces (2015)
  20. Morales-Enciso, Sergio; Branke, Juergen: Tracking global optima in dynamic environments with efficient global optimization (2015)

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