One of the key difficulties in using estimation-of-distribution algorithms is choosing the population sizes appropriately: Too small values lead to genetic drift, which can cause enormous difficulties. In the regime with no genetic drift, however, often the runtime is roughly proportional to the population size, which renders large population sizes inefficient. Based on a recent quantitative analysis which population sizes lead to genetic drift, we propose a parameter-less version of the compact genetic algorithm that automatically finds a suitable population size without spending too much time in situations unfavorable due to genetic drift. We prove an easy mathematical runtime guarantee for this algorithm and conduct an extensive experimental analysis on four classic benchmark problems. The former shows that under a natural assumption, our algorithm has a performance similar to the one obtainable from the best population size. The latter confirms that missing the right population size can be highly detrimental and shows that our algorithm as well as a previously proposed parameter-less one based on parallel runs avoids such pitfalls. Comparing the two approaches, ours profits from its ability to abort runs which are likely to be stuck in a genetic drift situation. CCS CONCEPTS • Theory of computation → Theory and algorithms for application domains; Theory of randomized search heuristics;