Stratified Bayesian Optimization
This work addresses optimization problems in simulation and engineering where function evaluations are costly and noisy, offering a domain-specific improvement.
The paper tackles derivative-free black-box global optimization of expensive noisy functions by leveraging strong dependence on a few influential random inputs, resulting in a new algorithm, Stratified Bayesian Optimization (SBO), that outperforms state-of-the-art Bayesian optimization benchmarks in numerical experiments.
We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong dependence to improve performance. Our algorithm is similar in spirit to stratification, a technique from simulation, which uses strong dependence on a categorical representation of the random input to reduce variance. We demonstrate in numerical experiments that SBO outperforms state-of-the-art Bayesian optimization benchmarks that do not leverage this dependence.