Abstract

The ???????????????????? package is a widely used for automatic algorithm configuration and implements various iterated racing procedures. The original ???????????????????? was designed for the optimisation of the solution quality reached within a given running time, a situation frequently arising when configuring algorithms such as stochastic local search procedures. However, when applied to configuration scenarios that involve minimising the running time of a given target algorithm, ???????????????????? falls short of reaching the performance of other general-purpose configuration approaches, since it tends to spend too much time evaluating poor configurations. In this article, we improve the efficacy of ???????????????????? in running time minimisation by integrating an adaptive capping mechanism into ????????????????????, inspired by the one used by ParamILS. We demonstrate that the resulting ???????????????????????????????? reaches performance levels competitive with those of state-of-the-art algorithm configurators that have been designed to perform well on running time minimisation scenarios. We also investigate the behaviour of ???????????????????????????????? in detail and contrast different ways of integrating adaptive capping.