  Bayesian optimization (BO) and its batch extensions are successful for
optimizing expensive black-box functions. However, these traditional BO
approaches are not yet ideal for optimizing less expensive functions when the
computational cost of BO can dominate the cost of evaluating the blackbox
function. Examples of these less expensive functions are cheap machine learning
models, inexpensive physical experiment through simulators, and acquisition
function optimization in Bayesian optimization. In this paper, we consider a
batch BO setting for situations where function evaluations are less expensive.
Our model is based on a new exploration strategy using geometric distance that
provides an alternative way for exploration, selecting a point far from the
observed locations. Using that intuition, we propose to use Sobol sequence to
guide exploration that will get rid of running multiple global optimization
steps as used in previous works. Based on the proposed distance exploration, we
present an efficient batch BO approach. We demonstrate that our approach
outperforms other baselines and global optimization methods when the function
evaluations are less expensive.
