  We study the $(1:s+1)$ success rule for controlling the population size of
the $(1,\lambda)$-EA. It was shown by Hevia Fajardo and Sudholt that this
parameter control mechanism can run into problems for large $s$ if the fitness
landscape is too easy. They conjectured that this problem is worst for the
OneMax benchmark, since in some well-established sense OneMax is known to be
the easiest fitness landscape. In this paper we disprove this conjecture and
show that OneMax is not the easiest fitness landscape with respect to finding
improving steps.
  As a consequence, we show that there exists $s$ and $\varepsilon$ such that
the self-adjusting $(1,\lambda)$-EA with $(1:s+1)$-rule optimizes OneMax
efficiently when started with $\varepsilon n$ zero-bits, but does not find the
optimum in polynomial time on Dynamic BinVal. Hence, we show that there are
landscapes where the problem of the $(1:s+1)$-rule for controlling the
population size of the $(1, \lambda)$-EA is more severe than for OneMax.
