Computer experiments and global optimization

By William J. Welch, Matthias Schonlau. 1997

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, neutral): “(stochastic process analysis of computer experiments) [133].”
Sequential kriging optimization using multiple-fidelity evaluations (2006, background): “ Schonlau (1997) suggested multiplying the value of the EI by the probability that the point is feasible.”
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization (2002, methodology): “suggests multiplying the value of the expected improvement by the probability that the point is feasible [ 12 ].”
Derivative-free optimization: a review of algorithms and comparison of software implementations (2012, methodology): “Efficient global optimization (EGO) The EGO algorithm [73,126] starts by performing a space-filling experimental design.”
Recent advances in surrogate-based optimization (2009, methodology): “A method of obtaining a specified number of infill points has been suggested by Schonlau [66].”
Constrained Efficient Global Optimization of Expensive Black-box Functions (2022, methodology): “…is where constraint violations are allowed to occur during the optimization process without penalty, for which there exists generic constrained Bayesian (efﬁ-cient global) optimization methods (Schonlau, 1997; Sasena et al., 2002; Basudhar et al., 2012; Bagheri et al., 2017; Zhang et al., 2021).”
Development of gradient-enhanced kriging approximations for multidisciplinary design optimization (2003, methodology): “4 used the Best Linear Unbiased Predictor (BLUP) presented by Schonlau[51].”
Sequential parameter optimization (2005, unknown): “the expected improvement) proposed by Schonlau [7].”