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• W2980370611 abstract "We consider the problem of stochastic simulation optimization with common random numbers over a numerical search domain. We propose the Knowledge Gradient for Common Random Numbers (KG-CRN) sequential sampling algorithm, a simple elegant modification to the Knowledge Gradient that incorporates the use of correlated noise in simulation outputs with Gaussian Process meta-models. We compare this method against the standard Knowledge Gradient and a more recently proposed variation that allows for pairwise sampling. Our method significantly outperforms both baselines under identical laboratory conditions while greatly reducing computational cost compared to pairwise sampling." @default.
• W2980370611 created "2019-10-25" @default.
• W2980370611 creator A5025287914 @default.
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• W2980370611 date "2019-10-21" @default.
• W2980370611 modified "2023-09-27" @default.
• W2980370611 title "Bayesian Optimization Allowing for Common Random Numbers" @default.
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