  Optimization of problems with high computational power demands is a
challenging task. A probabilistic approach to such optimization called Bayesian
optimization lowers performance demands by solving mathematically simpler model
of the problem. Selected approach, Gaussian Process, models problem using a
mixture of Gaussian functions. This paper presents specific modifications of
Gaussian Process optimization components from available scientific libraries.
Presented modifications were submitted to BlackBox 2020 challenge, where it
outperformed some conventionally available optimization libraries.
