  The $(1+(\lambda,\lambda))$ genetic algorithm is a recently proposed
single-objective evolutionary algorithm with several interesting properties. We
show that its main working principle, mutation with a high rate and crossover
as repair mechanism, can be transported also to multi-objective evolutionary
computation. We define the $(1+(\lambda,\lambda))$ global SEMO algorithm, a
variant of the classic global SEMO algorithm, and prove that it optimizes the
OneMinMax benchmark asymptotically faster than the global SEMO. Following the
single-objective example, we design a one-fifth rule inspired dynamic parameter
setting (to the best of our knowledge for the first time in discrete
multi-objective optimization) and prove that it further improves the runtime to
$O(n^2)$, whereas the best runtime guarantee for the global SEMO is only $O(n^2
\log n)$.
