Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

By Deng Huang, Theodore T. Allen, William I. Notz, Ning Zheng. 2006

707 citations recorded by the source corpus.

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

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “body of work devoted to extending the algorithm, particularly in adding constraints to the optimization problem [6], [23], [131], and in modeling noisy functions [14], [75], [76].”
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, background): “It is also possible to resample potential incumbents to get more reliable estimates of the values in a noisy environment [Bartz-Beielstein et al., 2005, Huang et al., 2006, Hutter et al., 2009], a process sometimes called intensification.”
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning (2014, methodology): “We used the ‘Gap’ measure of performance to compare the methods [24]:”
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2009, methodology): “The Eﬃcient Global Optimization (EGO) algorithm for optimizing expensive black-box functions is proposed by Jones et al. (1998) and extended to GPs by Huang et al. (2006).”
Gaussian Processes for Global Optimization (2008, methodology): “We use the “gap” measure of performance to compare the methods [9]”
Automated Algorithm Configuration and Parameter Tuning (2012, methodology): “…algorithm configuration (Hutter et al, 2007b, 2009a) lies Iterated Local Search (ILS), a well-known and versatile stochastic local search method that has been applied with great success to a wide range of difficult combinatorial problems (see, e.g., Lourenço et al, 2002; Hoos and Stützle, 2004).”
Automated Configuration of Mixed Integer Programming Solvers (2010, background): “Much existing work is restricted to scenarios having only relatively small numbers of numerical (often continuous) parameters, both in the model-based [7, 13, 17] and model-free [6, 1] literatures.”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “Sequential Kriging optimization (SKO) [95] is another extension of EGO to noisy function optimization and can be used to parameter tuning problem.”
A review of recent advances in global optimization (2009, methodology): “Huang et al. (2006) proposed a new global optimization method for stochastic black-box systems that is based on sequential kriging meta-models.”
An informational approach to the global optimization of expensive-to-evaluate functions (2006, methodology): “First introduced in (Jones et al. [1998]), this method has been used for computer experiments in (Sasena et al. [2002]), while modified criteria have been used in (Huang [2005]) and (Williams et al.”
Portfolio Allocation for Bayesian Optimization (2010, methodology): “To see how effective each method is at finding the global maximum, we use the “gap” metric [19], defined as”
A benchmark of kriging-based infill criteria for noisy optimization (2013, background): “The variety of applications has resulted in different approaches for Noisy Kriging-based Optimization (NKO) over the last years [10, 15, 39, 25, 26, 38, 35].”
A Review of Random Search Methods (2015, background): “[51], Sun et al.”
Kriging is well-suited to parallelize optimization (2010, positive): “For instance, [19] exposes some EGO-based methods for the optimization of noisy black-box functions like stochastic simulators.”