  Bayesian optimization is a powerful tool to optimize a black-box function,
the evaluation of which is time-consuming or costly. In this paper, we propose
a new approach to Bayesian optimization called GP-MGC, which maximizes
multiscale graph correlation with respect to the global maximum to determine
the next query point. We present our evaluation of GP-MGC in applications
involving both synthetic benchmark functions and real-world datasets and
demonstrate that GP-MGC performs as well as or even better than
state-of-the-art methods such as max-value entropy search and GP-UCB.
