  Very recently, the first mathematical runtime analyses for the NSGA-II, the
most common multi-objective evolutionary algorithm, have been conducted.
Continuing this research direction, we prove that the NSGA-II optimizes the
OneJumpZeroJump benchmark asymptotically faster when crossover is employed.
Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt,
this is the first time such an advantage of crossover is proven for the
NSGA-II. Our arguments can be transferred to single-objective optimization.
They then prove that crossover can speed up the $(\mu+1)$ genetic algorithm in
a different way and more pronounced than known before. Our experiments confirm
the added value of crossover and show that the observed advantages are even
larger than what our proofs can guarantee.
