  The study of hardest and easiest fitness landscapes is an active area of
research. Recently, Kaufmann, Larcher, Lengler and Zou conjectured that for the
self-adjusting $(1,\lambda)$-EA, Adversarial Dynamic BinVal (ADBV) is the
hardest dynamic monotone function to optimize. We introduce the function
Switching Dynamic BinVal (SDBV) which coincides with ADBV whenever the number
of remaining zeros in the search point is strictly less than $n/2$, where $n$
denotes the dimension of the search space. We show, using a combinatorial
argument, that for the $(1+1)$-EA with any mutation rate $p \in [0,1]$, SDBV is
drift-minimizing among the class of dynamic monotone functions. Our
construction provides the first explicit example of an instance of the
partially-ordered evolutionary algorithm (PO-EA) model with parameterized
pessimism introduced by Colin, Doerr and F\'erey, building on work of Jansen.
We further show that the $(1+1)$-EA optimizes SDBV in $\Theta(n^{3/2})$
generations. Our simulations demonstrate matching runtimes for both static and
self-adjusting $(1,\lambda)$ and $(1+\lambda)$-EA. We further show, using an
example of fixed dimension, that drift-minimization does not equal maximal
runtime.
