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This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps...