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 169 articles , 1 standard article )

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  1. 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)
  2. Davins-Valldaura, Joan; Moussaoui, Saïd; Pita-Gil, Guillermo; Plestan, Franck: ParEGO extensions for multi-objective optimization of expensive evaluation functions (2017)
  3. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  4. 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)
  5. 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)
  6. Steponavičė, Ingrida; Hyndman, Rob J.; Smith-Miles, Kate; Villanova, Laura: Dynamic algorithm selection for Pareto optimal set approximation (2017)
  7. Akhtar, Taimoor; Shoemaker, Christine A.: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection (2016)
  8. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  9. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  10. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  11. 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)
  12. Courrier, Nicolas; Boucard, Pierre-Alain; Soulier, Bruno: Variable-fidelity modeling of structural analysis of assemblies (2016)
  13. Cousin, Areski; Maatouk, Hassan; Rullière, Didier: Kriging of financial term-structures (2016)
  14. Durantin, Cédric; Marzat, Julien; Balesdent, Mathieu: Analysis of multi-objective Kriging-based methods for constrained global optimization (2016)
  15. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)
  16. Han, Bin; Ryzhov, Ilya O.; Defourny, Boris: Optimal learning in linear regression with combinatorial feature selection (2016)
  17. Jones, Matthew; Goldstein, Michael; Jonathan, Philip; Randell, David: Bayes linear analysis for Bayesian optimal experimental design (2016)
  18. Karademir, Serdar; Prokopyev, Oleg A.; Mailloux, Robert J.: Irregular polyomino tiling via integer programming with application in phased array antenna design (2016)
  19. Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.: SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems (2016)
  20. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)

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