A statistical method for global optimization

By D. Dennis, Susan John. 1992

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “There are many adaptive sampling approaches, including “Minimizing an Interpolating Surface (MIS)” (Jones, 2001), “Minimizing a Statistical Lower Bound (MSL)” (Cox and John, 1997), “Maximizing the Probability of Improvement (MPI)” (Stuckman, 1988) and “Maximizing Expected Improvement (MEI)” (Schonla”
A rigorous framework for optimization of expensive functions by surrogates (1998, background): “candidates, as in the paper by Cox and John (1997) .”
Portfolio Allocation for Bayesian Optimization (2010, background): “denote the PDF and CDF of the standard Normal distribution respectively and ˘is an optional trade-o parameter analogous to the one dened above. Upper condence bound (UCB & GP-UCB): Cox and John [10] introduce an algorithm they call \Sequential Design for Optimization", or SDO. Given a rando”
An informational approach to the global optimization of expensive-to-evaluate functions (2006, background): “Numerous illustrations of this superiority can be found in the literature (see, for instance, [ 6 ]) and many variations have been explored (for extensive surveys, see [13 ]a nd [17]).”
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels (2006, methodology): “Bayesian global optimization algorithms work in this way [ 44 ], [33].”
Variational Bayesian Optimization for Runtime Risk-Sensitive Control (2012, background): “Such criteria have been extensively studied in the context of statistical global optimization [5, 26] and economic decision making [16].”
Efficient Global Optimization of Expensive Black-Box Functions (1998, methodology): “Cox and John [ 9 ] use a statistical model, but they optimize the figure of merit by complete enumeration over a fine grid ‐ a procedure obviously limited to one or two dimensions.”
Surrogate-based methods for black-box optimization (2017, methodology): “This merit function is used in the so-called Sequential Design for Optimization (SDO) algorithm [16,17].”