  The compact Genetic Algorithm (cGA), parameterized by its hypothetical
population size $K$, offers a low-memory alternative to evolving a large
offspring population of solutions. It evolves a probability distribution,
biasing it towards promising samples. For the classical benchmark OneMax, the
cGA has to two different modes of operation: a conservative one with small step
sizes $\Theta(1/(\sqrt{n}\log n))$, which is slow but prevents genetic drift,
and an aggressive one with large step sizes $\Theta(1/\log n)$, in which
genetic drift leads to wrong decisions, but those are corrected efficiently. On
OneMax, an easy hill-climbing problem, both modes lead to optimization times of
$\Theta(n\log n)$ and are thus equally efficient.
  In this paper we study how both regimes change when we replace OneMax by the
harder hill-climbing problem DynamicBinVal. It turns out that the aggressive
mode is not affected and still yields quasi-linear runtime $O(n\cdot polylog
(n))$. However, the conservative mode becomes substantially slower, yielding a
runtime of $\Omega(n^2)$, since genetic drift can only be avoided with smaller
step sizes of $O(1/n)$. We complement our theoretical results with simulations.
