  Self-adjustment of parameters can significantly improve the performance of
evolutionary algorithms. A notable example is the $(1+(\lambda,\lambda))$
genetic algorithm, where the adaptation of the population size helps to achieve
the linear runtime on the OneMax problem. However, on problems which interfere
with the assumptions behind the self-adjustment procedure, its usage can lead
to performance degradation compared to static parameter choices. In particular,
the one fifth rule, which guides the adaptation in the example above, is able
to raise the population size too fast on problems which are too far away from
the perfect fitness-distance correlation.
  We propose a modification of the one fifth rule in order to have less
negative impact on the performance in scenarios when the original rule reduces
the performance. Our modification, while still having a good performance on
OneMax, both theoretically and in practice, also shows better results on linear
functions with random weights and on random satisfiable MAX-SAT instances.

Neural and Evolutionary Computing