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 186 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. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  3. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  4. Davins-Valldaura, Joan; Moussaoui, Saïd; Pita-Gil, Guillermo; Plestan, Franck: ParEGO extensions for multi-objective optimization of expensive evaluation functions (2017)
  5. Edwards, James; Fearnhead, Paul; Glazebrook, Kevin: On the identification and mitigation of weaknesses in the knowledge gradient policy for multi-armed bandits (2017)
  6. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  7. Hu, Ruimeng; Ludkovsk, Mike: Sequential design for ranking response surfaces (2017)
  8. 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)
  9. 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)
  10. Maatouk, Hassan; Bay, Xavier: Gaussian process emulators for computer experiments with inequality constraints (2017)
  11. 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)
  12. Müller, Juliane; Woodbury, Joshua D.: GOSAC: global optimization with surrogate approximation of constraints (2017)
  13. Namura, Nobuo; Shimoyama, Koji; Obayashi, Shigeru: Kriging surrogate model with coordinate transformation based on likelihood and gradient (2017)
  14. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  15. Scardua, Leonardo Azevedo; da Cruz, José Jaime: Complete offline tuning of the unscented Kalman filter (2017)
  16. Steponavičė, Ingrida; Hyndman, Rob J.; Smith-Miles, Kate; Villanova, Laura: Dynamic algorithm selection for Pareto optimal set approximation (2017)
  17. Tenne, Yoel: Machine-learning in optimization of expensive black-box functions (2017)
  18. Vu, Ky Khac; D’Ambrosio, Claudia; Hamadi, Youssef; Liberti, Leo: Surrogate-based methods for black-box optimization (2017)
  19. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Balancing global and local search in parallel efficient global optimization algorithms (2017)
  20. Zhan, Dawei; Qian, Jiachang; Cheng, Yuansheng: Pseudo expected improvement criterion for parallel EGO algorithm (2017)

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