Hybridizing the 1/5-th success rule with q-learning for controlling the mutation rate of an evolutionary algorithm

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.