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When faced with a specific optimization problem, choosing which algorithm touse is always a tough task. Not only is there a vast variety of algorithms toselect from, but these algorithms often are controlled by many hyperparameters,which need to be tuned in order to achieve the best performance possible.Usually, this problem is separated into two parts: algorithm selection andalgorithm configuration. With the significant advances made in MachineLearning, however, these problems can be integrated into a combined algorithmselection and hyperparameter optimization task, commonly known as the CASHproblem. In this work we compare sequential and integrated algorithm selectionand configuration approaches for the case of selecting and tuning the best outof 4608 variants of the Covariance Matrix Adaptation Evolution Strategy(CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite. We firstshow that the ranking of the modular CMA-ES variants depends to a large extenton the quality of the hyperparameters. This implies that even a sequentialapproach based on complete enumeration of the algorithm space will likelyresult in sub-optimal solutions. In fact, we show that the integrated approachmanages to provide competitive results at a much smaller computational cost. Wealso compare two different mixed-integer algorithm configuration techniques,called irace and Mixed-Integer Parallel Efficient Global Optimization(MIP-EGO). While we show that the two methods differ significantly in theirtreatment of the exploration-exploitation balance, their overall performancesare very similar.

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES