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 305 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. Bachoc, François; Broto, Baptiste; Gamboa, Fabrice; Loubes, Jean-Michel: Gaussian field on the symmetric group: prediction and learning (2020)
  4. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  5. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  6. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  7. Luo, Yangjun; Xing, Jian; Kang, Zhan: Topology optimization using material-field series expansion and Kriging-based algorithm: an effective non-gradient method (2020)
  8. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  9. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  10. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  11. Xiao, Ning-Cong; Yuan, Kai; Zhou, Chengning: Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables (2020)
  12. Yang, Xiu; Zhu, Xueyu; Li, Jing: When bifidelity meets cokriging: an efficient physics-informed multifidelity method (2020)
  13. Zhang, Mengchuang; Yao, Qin; Sun, Shouyi; Li, Lei; Hou, Xu: An efficient strategy for reliability-based multidisciplinary design optimization of twin-web disk with non-probabilistic model (2020)
  14. Barac, Diana; Multerer, Michael D.; Iber, Dagmar: Global optimization using Gaussian processes to estimate biological parameters from image data (2019)
  15. Bect, Julien; Bachoc, François; Ginsbourger, David: A supermartingale approach to Gaussian process based sequential design of experiments (2019)
  16. Bouttier, Clément; Gavra, Ioana: Convergence rate of a simulated annealing algorithm with noisy observations (2019)
  17. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  18. Chen, Ye; Ryzhov, Ilya O.: Complete expected improvement converges to an optimal budget allocation (2019)
  19. Fuhg, Jan N.; Fau, Amélie: Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type (2019)
  20. Hristov, P. O.; DiazDelaO, F. A.; Farooq, U.; Kubiak, K. J.: Adaptive Gaussian process emulators for efficient reliability analysis (2019)

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