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Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in real-world optimization tasks. However, previous theoretical studies often employ EAs with only a parent or offspring population and focus on specific problems. Furthermore, they often only show upper bounds on the running time, while lower bounds are also necessary to get a complete understanding of an algorithm. In this paper, we analyze the running time of the ()-EA (a general population-based EA with mutation only) on the class of pseudo-Boolean functions with a unique global optimum. By applying the recently proposed switch analysis approach, we prove the lower bound for the first time. Particularly on the two widely-studied problems, OneMax and LeadingOnes, the derived lower bound reveals that the ()-EA will be slower than the (1 …