Gaussian Process-based Bayesian Optimization and Shape Transformation of Benchmark Functions

Gaussian process-based Bayesian optimization (GPBO) finds application in various fields for approximate optimization of parameters. Because the search performance depends on the shape of the black-box function, users of GPBO should know these details. Therefore, we provide some experiment results of the relationship between GPBO search performance and the shape of the black-box function. We adopted "Easom," "Ackley," "Bukin N.6," "Beale," "Rosenbrock," and "Goldstein-Price," which are benchmark functions for optimization problems. Moreover, we adopted logarithmic and range-transformed functions to provide deeper insight.