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Combining sequential model-based algorithm configuration with default-guided probabilistic sampling

General-purpose automated algorithm configuration procedures have enabled impressive improvements in the state of the art in solving challenging problems from AI, operations research and other areas. The most successful configurators combine multiple techniques to search vast combinatorial spaces of parameter settings for a given algorithm as efficiently as possible. Specifically, two of the most prominent general-purpose algorithm configurators, SMAC and irace, can be seen as combinations of Bayesian optimisation and racing, and of racing and an estimation of distribution algorithm, respectively. Here, we investigate an approach that combines all three of these techniques into one single configurator, while exploiting prior knowledge contained in expert-chosen default parameter values. We demonstrate significant performance improvements over irace and SMAC on a broad range of running time optimisation scenarios from AClib.