The process begins by selecting an initial set of points to evaluate the target function, often using a design of experiments method. A surrogate model, typically a Gaussian Process, is then constructed to approximate the target function. This surrogate model is updated iteratively as new data points are evaluated, guiding the search for the optimal solution.
