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 371 articles , 1 standard article )

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  1. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  2. Bliek, Laurens; Verwer, Sicco; de Weerdt, Mathijs: Black-box combinatorial optimization using models with integer-valued minima (2021)
  3. Carballo, Alba; Durban, Maria; Kauermann, Göran; Lee, Dae-Jin: A general framework for prediction in penalized regression (2021)
  4. Chu, Sheng; Xiao, Mi; Gao, Liang; Zhang, Yan; Zhang, Jinhao: Robust topology optimization for fiber-reinforced composite structures under loading uncertainty (2021)
  5. Hao, Peng; Feng, Shaojun; Liu, Hao; Wang, Yutian; Wang, Bo; Wang, Bin: A novel nested stochastic Kriging model for response noise quantification and reliability analysis (2021)
  6. Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif: Dataset2Vec: learning dataset meta-features (2021)
  7. Kostorz, Wawrzyniec: A practical method for well log data classification (2021)
  8. Mathesen, Logan; Pedrielli, Giulia; Ng, Szu Hui; Zabinsky, Zelda B.: Stochastic optimization with adaptive restart: a framework for integrated local and global learning (2021)
  9. Müller, Juliane; Park, Jangho; Sahu, Reetik; Varadharajan, Charuleka; Arora, Bhavna; Faybishenko, Boris; Agarwal, Deborah: Surrogate optimization of deep neural networks for groundwater predictions (2021)
  10. Pandita, Piyush; Tsilifis, Panagiotis; Awalgaonkar, Nimish M.; Bilionis, Ilias; Panchal, Jitesh: Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian processes (2021)
  11. Peng, Yijie; Chen, Chun-Hung; Fu, Michael C.; Hu, Jian-Qiang; Ryzhov, Ilya O.: Efficient sampling allocation procedures for optimal quantile selection (2021)
  12. Sabater, Christian; Le Maître, Olivier; Congedo, Pietro Marco; Görtz, Stefan: A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources (2021)
  13. Sinsbeck, Michael; Cooke, Emily; Nowak, Wolfgang: Sequential design of computer experiments for the computation of Bayesian model evidence (2021)
  14. Soize, C.; Ghanem, R.: Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets (2021)
  15. Wang, Hu; Hu, Wei; Li, Enying: Handling of constraints in efficient global optimization (2021)
  16. Zhang, Xinshuai; Xie, Fangfang; Ji, Tingwei; Zhu, Zaoxu; Zheng, Yao: Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (2021)
  17. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  18. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  19. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  20. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)

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