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 252 articles , 1 standard article )

Showing results 1 to 20 of 252.
Sorted by year (citations)

1 2 3 ... 11 12 13 next

  1. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)
  2. Letham, Benjamin; Karrer, Brian; Ottoni, Guilherme; Bakshy, Eytan: Constrained Bayesian optimization with noisy experiments (2019)
  3. Picheny, Victor; Binois, Mickael; Habbal, Abderrahmane: A Bayesian optimization approach to find Nash equilibria (2019)
  4. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  5. Balakin, D. A.: Numerical methods for computing plausibility and belief distributions of consequences of a subjective model of object of research (2018)
  6. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  7. Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm (2018)
  8. Damblin, Guillaume; Barbillon, Pierre; Keller, Merlin; Pasanisi, Alberto; Parent, Éric: Adaptive numerical designs for the calibration of computer codes (2018)
  9. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  10. Haaland, Benjamin; Wang, Wenjia; Maheshwari, Vaibhav: A framework for controlling sources of inaccuracy in Gaussian process emulation of deterministic computer experiments (2018)
  11. He, Xinyu; Powell, Warren B.: Optimal learning for stochastic optimization with nonlinear parametric belief models (2018)
  12. Horn, Daniel; Demircioğlu, Aydın; Bischl, Bernd; Glasmachers, Tobias; Weihs, Claus: A comparative study on large scale kernelized support vector machines (2018)
  13. Jones, Matthew; Goldstein, Michael; Jonathan, Philip; Randell, David: Bayes linear analysis of risks in sequential optimal design problems (2018)
  14. Kieslich, Chris A.; Boukouvala, Fani; Floudas, Christodoulos A.: Optimization of black-box problems using Smolyak grids and polynomial approximations (2018)
  15. Liu, Xiao-Xiao; Wang, Yuan-Sheng: A new formulation on seismic risk assessment for reinforced concrete structures with both random and bounded uncertainties (2018)
  16. Marmin, Sébastien; Ginsbourger, David; Baccou, Jean; Liandrat, Jacques: Warped Gaussian processes and derivative-based sequential designs for functions with heterogeneous variations (2018)
  17. Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
  18. Nedělková, Zuzana; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith: Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space (2018)
  19. Nuñez, Luigi; Regis, Rommel G.; Varela, Kayla: Accelerated random search for constrained global optimization assisted by radial basis function surrogates (2018)
  20. Pearce, Michael; Branke, Juergen: Continuous multi-task Bayesian optimisation with correlation (2018)

1 2 3 ... 11 12 13 next