Analysis of coevolution for worst-case optimization

The problem of finding entities with the best worst-case performance across multiple scenarios arises in domains ranging from job shop scheduling to designing physical artifacts. In spite of previous successful applications of evolutionary computation techniques, particularly coevolution, to such domains, little work has examined utilizing coevolution for optimizing worst-case behavior. Previous work assesses certain algorithm mechanisms using aggregate performance on test problems. We examine fitness and population trajectories of individual algorithm runs, making two observations: first, that aggregate plots wash out important effects that call into question what these algorithms can produce; and second, that none of the mechanisms is generally better than the rest. More importantly, our dynamics analysis explains how the interplay of algorithm properties and problem properties influences performance. These contributions argue in favor of a reassessment of what makes for a good worst-case coevolutionary algorithm and suggest how to design one.