  It is well known that evolutionary algorithms (EAs) achieve peak performance
only when their parameters are suitably tuned to the given problem. Even more,
it is known that the best parameter values can change during the optimization
process. Parameter control mechanisms are techniques developed to identify and
to track these values.
  Recently, a series of rigorous theoretical works confirmed the superiority of
several parameter control techniques over EAs with best possible static
parameters. Among these results are examples for controlling the mutation rate
of the $(1+\lambda)$~EA when optimizing the OneMax problem. However, it was
shown in [Rodionova et al., GECCO'19] that the quality of these techniques
strongly depends on the offspring population size $\lambda$.
  We introduce in this work a new hybrid parameter control technique, which
combines the well-known one-fifth success rule with Q-learning. We demonstrate
that our HQL mechanism achieves equal or superior performance to all techniques
tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous
parameter control methods -- simultaneously for all offspring population sizes
$\lambda$. We also show that the promising performance of HQL is not restricted
to OneMax, but extends to several other benchmark problems.
