  Two mechanisms have recently been proposed that can significantly speed up
finding distant improving solutions via mutation, namely using a random
mutation rate drawn from a heavy-tailed distribution ("fast mutation", Doerr et
al. (2017)) and increasing the mutation strength based on stagnation detection
(Rajabi and Witt (2020)). Whereas the latter can obtain the asymptotically best
probability of finding a single desired solution in a given distance, the
former is more robust and performs much better when many improving solutions in
some distance exist.
  In this work, we propose a mutation strategy that combines ideas of both
mechanisms. We show that it can also obtain the best possible probability of
finding a single distant solution. However, when several improving solutions
exist, it can outperform both the stagnation-detection approach and fast
mutation. The new operator is more than an interleaving of the two previous
mechanisms and it also outperforms any such interleaving.
