  One hope when using non-elitism in evolutionary computation is that the
ability to abandon the current-best solution aids leaving local optima. To
improve our understanding of this mechanism, we perform a rigorous runtime
analysis of a basic non-elitist evolutionary algorithm (EA), the
$(\mu,\lambda)$ EA, on the most basic benchmark function with a local optimum,
the jump function. We prove that for all reasonable values of the parameters
and the problem, the expected runtime of the $(\mu,\lambda)$~EA is, apart from
lower order terms, at least as large as the expected runtime of its elitist
counterpart, the $(\mu+\lambda)$~EA (for which we conduct the first runtime
analysis on jump functions to allow this comparison). Consequently, the ability
of the $(\mu,\lambda)$~EA to leave local optima to inferior solutions does not
lead to a runtime advantage.
  We complement this lower bound with an upper bound that, for broad ranges of
the parameters, is identical to our lower bound apart from lower order terms.
This is the first runtime result for a non-elitist algorithm on a multi-modal
problem that is tight apart from lower order terms.
