Abstract. Evolutionary Algorithms (EAs) are population-based randomized optimizers often solving problems quite successfully. Here, the focus is on the possible effects of changing the parent population size. Therefore, new functions are presented where for a simple mutationbased EA even a decrease of the population size by one leads from an efficient optimization to an enormous running time with an overwhelming probability. This is proven rigorously for all feasible population sizes. In order to obtain these results, new methods for the analysis of the EA are developed.