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 362 articles , 1 standard article )

Showing results 41 to 60 of 362.
Sorted by year (citations)

previous 1 2 3 4 5 ... 17 18 19 next

  1. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  2. Verma, Aekaansh; Wong, Kwai; Marsden, Alison L.: A concurrent implementation of the surrogate management framework with application to cardiovascular shape optimization (2020)
  3. Wang, Jialei; Clark, Scott C.; Liu, Eric; Frazier, Peter I.: Parallel Bayesian global optimization of expensive functions (2020)
  4. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)
  5. Wauters, Jolan; Keane, Andy; Degroote, Joris: Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization (2020)
  6. Xiao, Ning-Cong; Yuan, Kai; Zhou, Chengning: Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables (2020)
  7. Yang, Xiu; Zhu, Xueyu; Li, Jing: When bifidelity meets cokriging: an efficient physics-informed multifidelity method (2020)
  8. Yuan, Yuan; Li, Zukui; Huang, Biao: Oil sands extraction plant debottlenecking: an optimization approach (2020)
  9. Zafar, Tayyab; Zhang, Yanwei; Wang, Zhonglai: An efficient Kriging based method for time-dependent reliability based robust design optimization via evolutionary algorithm (2020)
  10. Zhan, Dawei; Xing, Huanlai: Expected improvement for expensive optimization: a review (2020)
  11. 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)
  12. Barac, Diana; Multerer, Michael D.; Iber, Dagmar: Global optimization using Gaussian processes to estimate biological parameters from image data (2019)
  13. Bect, Julien; Bachoc, François; Ginsbourger, David: A supermartingale approach to Gaussian process based sequential design of experiments (2019)
  14. Bouttier, Clément; Gavra, Ioana: Convergence rate of a simulated annealing algorithm with noisy observations (2019)
  15. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  16. Chen, Ye; Ryzhov, Ilya O.: Complete expected improvement converges to an optimal budget allocation (2019)
  17. Cortesi, Andrea F.; Jannoun, Ghina; Congedo, Pietro M.: Kriging-sparse polynomial dimensional decomposition surrogate model with adaptive refinement (2019)
  18. Fuhg, Jan N.; Fau, Amélie: Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type (2019)
  19. Hristov, P. O.; DiazDelaO, F. A.; Farooq, U.; Kubiak, K. J.: Adaptive Gaussian process emulators for efficient reliability analysis (2019)
  20. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)

previous 1 2 3 4 5 ... 17 18 19 next