A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions

By Rommel G. Regis, Christine Annette Shoemaker. INFORMS journal on computing, 2007

521 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20d1574e87443dc1cd69cbeabc9389489b2703b1

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Citations in the corpus

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019, background): “, polynomial basis functions [9], radial basis functions [10], Gaussian process (GP) [11, 12], and stochastic polynomial chaos expansion (PCE) [13, 14], among others.”
Surrogate-based methods for black-box optimization (2017, methodology): “In [47], two speciﬁc implementations are discussed: Global Metric Stochastic Response Surface (GMSRS) and Local Metric Stochastic Response Surface (LMSRS).”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “N = 2(n+1) (Regis and Shoemaker 2007) Depend on metamodeling property N = max[2(n+1), 0.”
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “…al., 2001; Sacks et al., 1989), support vector machines (SVMs) (Willcox and Megretski, 2005), radial basis functions (RBFs) (Hussain et al., 2002; Regis and Shoemaker, 2007; Mullur and Messac, 2006), and neural networks (NNs) (Tompson et al., 2017; Alam et al., 2004; Khu and Werner, 2003; Gilmer…”
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 (2021, methodology): “We see that TuRBO 8 performs the best with a score of 88.921 followed by pySOT which uses the stochastic RBF (SRBF) method [56].”
Assessing the impacts of parameter uncertainty for computationally expensive groundwater models (2006, methodology): “The FA optimization method is related to an algorithm developed by Regis and Shoemaker [2005] and is described in detail in other works [Regis, 2004; Regis and Shoemaker, 2006 ].”
Using response surfaces and expected improvement to optimize snake robot gait parameters (2011, methodology): “In this paper, we focus on Gaussian process techniques (see [11] for a more complete treatment) in fitting response surfaces for the performance of snake robot gaits.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, unknown): “…models, such as kriging models (Joseph et al. 2006; Martin and Simpson 2005), radial basis functions (RBF) models (Fang and Horstemeyer 2006; Regis and Shoemaker 2007a, b), response surface models (Hill and Hunter 1966; Kaufman et al. 1996), support vector machine (Collobert and Bengio…”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “A quick hyperparameter estimation is of particular importance in optimization, where the radial-basis model is updated in each iteration of the optimization process [1, 174, 175].”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “…2009), groundwater systems analysis and management (Bau and Mayer, 2006; Fen et al., 2009; Johnson and Rogers, 2000; Kourakos and Mantoglou, 2009; Regis and Shoemaker, 2004, 2007a,b, 2009; Yan and Minsker, 2006), and water distribution network design and optimization (Behzadian et al., 2009;…”
Review of surrogate modeling in water resources (2012, methodology): “…toward the already explored regions of the feasible space (probably containing local optima) and could be quite misleading; therefore, the unexplored regions would likely remain unexplored when optimizing on such metamodels [Jin et al., 2002; Regis and Shoemaker, 2007a; Yan and Minsker, 2006].”
Scalable Global Optimization via Local Bayesian Optimization (2019, methodology): “When using TuRBO - 1 , we consider an improvement from at least one evaluation in the batch a success [37].”