  One of the first and easy to use techniques for proving run time bounds for
evolutionary algorithms is the so-called method of fitness levels by Wegener.
It uses a partition of the search space into a sequence of levels which are
traversed by the algorithm in increasing order, possibly skipping levels. An
easy, but often strong upper bound for the run time can then be derived by
adding the reciprocals of the probabilities to leave the levels (or upper
bounds for these). Unfortunately, a similarly effective method for proving
lower bounds has not yet been established. The strongest such method, proposed
by Sudholt (2013), requires a careful choice of the viscosity parameters
$\gamma_{i,j}$, $0 \le i < j \le n$.
  In this paper we present two new variants of the method, one for upper and
one for lower bounds. Besides the level leaving probabilities, they only rely
on the probabilities that levels are visited at all. We show that these can be
computed or estimated without greater difficulties and apply our method to
reprove the following known results in an easy and natural way. (i) The precise
run time of the (1+1) EA on \textsc{LeadingOnes}. (ii) A lower bound for the
run time of the (1+1) EA on \textsc{OneMax}, tight apart from an $O(n)$ term.
(iii) A lower bound for the run time of the (1+1) EA on long $k$-paths. We also
prove a tighter lower bound for the run time of the (1+1) EA on jump functions
by showing that, regardless of the jump size, only with probability $O(2^{-n})$
the algorithm can avoid to jump over the valley of low fitness.
