A global search method for optimizing nonlinear systems

By Bruce E. Stuckman. 1988

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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 Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, methodology): “Later work extended Kushner’s technique to multidimensional optimization, using, for example, interpolation in a Delauney triangulation of the space [Elder, 1992] or projecting Wiener processes between sample points [Stuckman, 1988].”
Recent Advances in Bayesian Optimization (2022, background): “Stuckman [231], Perttunen [196] and Elder [64] extended Kushner’s work to high-dimensional problems.”
A Taxonomy of Global Optimization Methods Based on Response Surfaces (2001, methodology): “Kushner's original algorithm was one dimensional, but Stuckman (1988), Perttunen (1991), Elder (1992), and Mockus (1994), have all heuristically extended the method to higher dimensions.”
Efficient Global Optimization of Expensive Black-Box Functions (1998, methodology): “The algorithms of Elder [16], Perttunen [27] and Stuckman [33] are heuristic extensions of Kushner’s one-dimensional algorithm [19] and, as such, do not use any statistical model at all.”
An experimental methodology for response surface optimization methods (2011, background): “Work on probabilistic response surface methods has been steady over the past 40 years (Kushner 1964; Perttunen et al. 1989; Stuckman 1989; Mockus 1989; Sacks et al. 1989; Elder 1992; Cox and John 1995; Schonlau 1997; Jones and Schonlau 1998; Gutmann 2001; Sasena 2002; Santner et al. 2003; Boyle…”
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model (2010, background): “2033671 such as the expected improvement [6] and the probability of improvement [7] for evaluation of the objective function at a new point could be defined and calculated for determining which point should be evaluated next.”
The Knowledge-Gradient Policy for Correlated Normal Beliefs (2009, methodology): “Later work (see Stuckman 1988; Mockus 1989, 1994) extended these two methods to multiple dimensions while continuing to use the Wiener process prior.”
Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. (2002, methodology): “Stuckman’s implementation [91] of the P-algorithm used 1-D projections of the Wiener process along lines connecting each sample point.”
A new optimization method: Big Bang-Big Crunch (2006, background): “This one step convergence is superior compared to selecting two members and ﬁnding their center of gravity [14,15].”