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Bayesian optimization allowing for common random numbers
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Pearce , Michael, Policzek, Matthias and Branke, Jürgen (2021) Bayesian optimization allowing for common random numbers. Operations Research . (In Press)
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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.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||
| Journal or Publication Title: | Operations Research | ||||||
| Publisher: | INFORMS | ||||||
| ISSN: | 0030-364X | ||||||
| Official Date: | 2021 | ||||||
| Dates: |
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| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Related URLs: | |||||||
| Open Access Version: |
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