A Bayesian approach to constrained single- and multi-objective optimization

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can...

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Vydané v:	Journal of global optimization Ročník 67; číslo 1-2; s. 97 - 133
Hlavní autori:	Feliot, Paul, Bect, Julien, Vazquez, Emmanuel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2017 Springer Springer Nature B.V Springer Verlag
Predmet:	Algorithms Analysis Approximation Bayesian analysis Computation Computer Science Computer simulation Constraints Criteria Design optimization Machine Learning Mathematical functions Mathematics Mathematics and Statistics Monte Carlo simulation Multiple objective analysis Operations Research Operations Research/Decision Theory Optimization Performance evaluation Real Functions Sampling Simulation Simulation methods Statistics Studies Subset simulation Gaussian process Expected improvement Sequential Monte Carlo Multi-objective Bayesian optimization Kriging sequential Monte Carlo subset simulation kriging Gaussian processes multi-objective optimization expected improvement
ISSN:	0925-5001, 1573-2916
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