BO is well-known as a optimization method suitable for searching in a high dimensional space with objective functions that are expensive to calculate. In general, this technique aims to build a surrogate model f (x) to approximate the distribution of the target function. This model is usually a Gaussian process G P(µ(x), k(x, x ′ )) that is iteratively refined with new samples x and their corresponding target function values ŷ using the Bayes' rule. Beside the surrogate model, an acquisition function is used to select the next candidates x to observe that can bring the best updates to the surrogate model.