  Evolutionary algorithms are known to be robust to noise in the evaluation of
the fitness. In particular, larger offspring population sizes often lead to
strong robustness. We analyze to what extent the $(1+(\lambda,\lambda))$
genetic algorithm is robust to noise. This algorithm also works with larger
offspring population sizes, but an intermediate selection step and a
non-standard use of crossover as repair mechanism could render this algorithm
less robust than, e.g., the simple $(1+\lambda)$ evolutionary algorithm. Our
experimental analysis on several classic benchmark problems shows that this
difficulty does not arise. Surprisingly, in many situations this algorithm is
even more robust to noise than the $(1+\lambda)$~EA.
