On the Design of Optimization Strategies Based on Global Response Surface Approximation Models

By András Sóbester, Stephen J. Leary, Andy J. Keane. 2005

320 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Molecular Biology

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “For example, S obester et al. (2005) suggested that about 35% of the samples should be spent in the initial stage, while Jones et al. (1998) recommended that the initial sample size should be about ten times the dimensionality of the problem.”
Infill sampling criteria for surrogate-based optimization with constraint handling (2012, methodology): “To name a few: generalized expected improvement (Schonlau 1997, Sasena et al. 2002) and weighted expected improvement (Sóbester et al. 2005) assign different weightings to exploration and exploitation; quantile-based expected improvement (Picheny et al. 2010) deals with noisy experiments; and…”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “Such ameasure is explicitly available in some metamodels, e.g., in polynomials, kriging (DACE), Gaussian Radial Basis Function models (Sobester et al., 2005), and smoothing spline ANOVA (Gu, 2002).”
Recent advances in surrogate-based optimization (2009, methodology): “[73] used a weighted expected improvement formulation as part of a twostage approach to achieve similar ends, while Forrester [18] used a weighted statistical lower bound with reinforcement learning to chose the weighting.”
Kriging Hyperparameter Tuning Strategies (2008, methodology): “[19], each of these budgets has one-third of the evaluations reserved for the initial design of experiments, with the remainder used in the update process.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “Then it switches to (Sóbester et al. 2005) Depend on computing budget N = 0.5 N max (Aute et al. 2013) H. Liu et al. pure local exploitation in interesting regions for improving the model accuracy efficiently.”
Using response surfaces and expected improvement to optimize snake robot gait parameters (2011, background): “This quality of the likelihood function causes simple search techniques, such as conjugate gradient, to often terminate early, or reach an incorrect local optimum.”
Review of surrogate modeling in water resources (2012, methodology): “Although globally convergent 13 of 32 under some mild assumptions [Sobester et al., 2005], the framework solely using the approximation uncertainty would require impractically large number of original function evaluations, especially when the number of decision variables is more than a few.”