Abstract
Understanding the impact of crossover in evolutionary algorithms is one of the major challenges in the theoretical analysis of these stochastic search algorithms. Recently, it has been shown that crossover provably helps to speed up evolutionary algorithms for the classical allpairs-shortest path (APSP) problem. In this paper, we extend this approach to the NP-hard multi-criteria APSP problem. Based on rigorous runtime analyses, we point out that crossover leads to better worst case bounds than previous known results. This is the first time that rigorous runtime analyses have shown the usefulness of crossover for an NP-hard multi-criteria optimization problem.
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