  It is an ongoing debate whether and how comma selection in evolutionary
algorithms helps to escape local optima. We propose a new benchmark function to
investigate the benefits of comma selection: OneMax with randomly planted local
optima, generated by frozen noise. We show that comma selection (the
$(1,\lambda)$ EA) is faster than plus selection (the $(1+\lambda)$ EA) on this
benchmark, in a fixed-target scenario, and for offspring population sizes
$\lambda$ for which both algorithms behave differently. For certain parameters,
the $(1,\lambda)$ EA finds the target in $\Theta(n \ln n)$ evaluations, with
high probability (w.h.p.), while the $(1+\lambda)$ EA) w.h.p. requires almost
$\Theta((n\ln n)^2)$ evaluations.
  We further show that the advantage of comma selection is not arbitrarily
large: w.h.p. comma selection outperforms plus selection at most by a factor of
$O(n \ln n)$ for most reasonable parameter choices. We develop novel methods
for analysing frozen noise and give powerful and general fixed-target results
with tail bounds that are of independent interest.
