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 412 articles , 1 standard article )

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  1. Anahideh, Hadis; Rosenberger, Jay; Chen, Victoria: High-dimensional black-box optimization under uncertainty (2022)
  2. Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio: Symbolic DNN-tuner (2022)
  3. Hayashi, Shogo; Honda, Junya; Kashima, Hisashi: Bayesian optimization with partially specified queries (2022)
  4. Hong, Linxiong; Li, Huacong; Fu, Jiangfeng: A novel surrogate-model based active learning method for structural reliability analysis (2022)
  5. Krokos, Vasilis; Xuan, Viet Bui; Bordas, Stéphane P. A.; Young, Philippe; Kerfriden, Pierre: A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features (2022)
  6. Mohammadi, Hossein; Challenor, Peter; Williamson, Daniel; Goodfellow, Marc: Cross-validation-based adaptive sampling for Gaussian process models (2022)
  7. Morita, Y.; Rezaeiravesh, S.; Tabatabaei, N.; Vinuesa, R.; Fukagata, K.; Schlatter, P.: Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems (2022)
  8. Ollier, Edouard: Fast selection of nonlinear mixed effect models using penalized likelihood (2022)
  9. Paun, L. Mihaela; Husmeier, Dirk: Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models (2022)
  10. Sinou, J.-J.; Denimal, E.: Reliable crack detection in a rotor system with uncertainties via advanced simulation models based on kriging and polynomial chaos expansion (2022)
  11. Soize, Christian: Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints (2022)
  12. Toscano-Palmerin, Saul; Frazier, Peter I.: Bayesian optimization with expensive integrands (2022)
  13. Wang, Wenyu; Akhtar, Taimoor; Shoemaker, Christine A.: Integrating (\varepsilon)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems (2022)
  14. Zhai, Jianyuan; Boukouvala, Fani: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (2022)
  15. Zheng, Liang; Bao, Ji; Xu, Chengcheng; Tan, Zhen: Biobjective robust simulation-based optimization for unconstrained problems (2022)
  16. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  17. Ban, Naohiko; Yamazaki, Wataru: Efficient global optimization method via clustering/classification methods and exploration strategy (2021)
  18. Bemporad, Alberto; Piga, Dario: Global optimization based on active preference learning with radial basis functions (2021)
  19. Blanchard, Antoine; Sapsis, Themistoklis: Bayesian optimization with output-weighted optimal sampling (2021)
  20. Bliek, Laurens; Verwer, Sicco; de Weerdt, Mathijs: Black-box combinatorial optimization using models with integer-valued minima (2021)

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