  In recent years, leveraging parallel and distributed computational resources
has become essential to solve problems of high computational cost. Bayesian
optimization (BO) has shown attractive results in those expensive-to-evaluate
problems such as hyperparameter optimization of machine learning algorithms.
While many parallel BO methods have been developed to search efficiently
utilizing these computational resources, these methods assumed synchronous
settings or were not scalable. In this paper, we propose a simple and scalable
BO method for asynchronous parallel settings. Experiments are carried out with
a benchmark function and hyperparameter optimization of multi-layer
perceptrons, which demonstrate the promising performance of the proposed
method.
