Bayesian Simulation Optimization with Common Random Numbers
- Resource Type
- Authors
- Juergen Branke; Michael Pearce; Matthias Poloczek
- Source
- WSC
- Subject
- 021103 operations research
Computer science
Bayesian probability
0211 other engineering and technologies
Sampling (statistics)
02 engineering and technology
010501 environmental sciences
01 natural sciences
Domain (software engineering)
symbols.namesake
Simple (abstract algebra)
Stochastic simulation
symbols
Pairwise comparison
Noise (video)
Gaussian process
Algorithm
0105 earth and related environmental sciences
- Language
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.