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 316 articles , 1 standard article )

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  1. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  2. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  3. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  4. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)
  5. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  6. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  7. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  8. Broto, Baptiste; Bachoc, François; Depecker, Marine: Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution (2020)
  9. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  10. Chen, Ray-Bing; Wang, Yuan; Wu, C. F. Jeff: Finding optimal points for expensive functions using adaptive RBF-based surrogate model via uncertainty quantification (2020)
  11. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  12. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  13. Luo, Yangjun; Xing, Jian; Kang, Zhan: Topology optimization using material-field series expansion and Kriging-based algorithm: an effective non-gradient method (2020)
  14. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  15. Rodriguez, Sergio; Ludkovski, Michael: Probabilistic bisection with spatial metamodels (2020)
  16. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  17. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  18. Tan, Matthias H. Y.: Bayesian optimization of expected quadratic loss for multiresponse computer experiments with internal noise (2020)
  19. Tian, Kuo; Li, Zengcong; Huang, Lei; Du, Kaifan; Jiang, Liangliang; Wang, Bo: Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering (2020)
  20. Xiao, Ning-Cong; Yuan, Kai; Zhou, Chengning: Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables (2020)

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