  The $(1 + (\lambda,\lambda))$ genetic algorithm is a younger evolutionary
algorithm trying to profit also from inferior solutions. Rigorous runtime
analyses on unimodal fitness functions showed that it can indeed be faster than
classical evolutionary algorithms, though on these simple problems the gains
were only moderate.
  In this work, we conduct the first runtime analysis of this algorithm on a
multimodal problem class, the jump functions benchmark. We show that with the
right parameters, the \ollga optimizes any jump function with jump size $2 \le
k \le n/4$ in expected time $O(n^{(k+1)/2} e^{O(k)} k^{-k/2})$, which
significantly and already for constant~$k$ outperforms standard mutation-based
algorithms with their $\Theta(n^k)$ runtime and standard crossover-based
algorithms with their $\tilde{O}(n^{k-1})$ runtime guarantee.
  For the isolated problem of leaving the local optimum of jump functions, we
determine provably optimal parameters that lead to a runtime of $(n/k)^{k/2}
e^{\Theta(k)}$. This suggests some general advice on how to set the parameters
of the \ollga, which might ease the further use of this algorithm.
