Stagnation Detection Meets Fast Mutation
This work addresses a specific bottleneck in evolutionary computation for researchers, offering an incremental improvement over existing mutation mechanisms.
The paper tackles the problem of improving mutation strategies in evolutionary algorithms by combining fast mutation and stagnation detection, showing that the new hybrid operator achieves the best possible probability of finding a single distant solution and outperforms both previous methods when multiple improving solutions exist.
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.