  Runtime analysis has produced many results on the efficiency of simple
evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in
evolutionary multiobjective optimisation (EMO). Recently, the first runtime
analyses of the famous and highly cited EMO algorithm NSGA-II have emerged,
demonstrating that practical algorithms with thousands of applications can be
rigorously analysed. However, these results only show that NSGA-II has the same
performance guarantees as GSEMO and it is unclear how and when NSGA-II can
outperform GSEMO. We study this question in noisy optimisation and consider a
noise model that adds large amounts of posterior noise to all objectives with
some constant probability $p$ per evaluation. We show that GSEMO fails badly on
every noisy fitness function as it tends to remove large parts of the
population indiscriminately. In contrast, NSGA-II is able to handle the noise
efficiently on \textsc{LeadingOnesTrailingZeroes} when $p<1/2$, as the
algorithm is able to preserve useful search points even in the presence of
noise. We identify a phase transition at $p=1/2$ where the expected time to
cover the Pareto front changes from polynomial to exponential. To our
knowledge, this is the first proof that NSGA-II can outperform GSEMO and the
first runtime analysis of NSGA-II in noisy optimisation.
