  In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate
marginal distribution algorithm (UMDA) needs time exponential in the parent
populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They
conclude from this result that univariate EDAs have difficulties with deception
and epistasis.
  In this work, we show that this negative finding is caused by an unfortunate
choice of the parameters of the UMDA. When the population sizes are chosen
large enough to prevent genetic drift, then the UMDA optimizes the DLB problem
with high probability with at most $\lambda(\frac{n}{2} + 2 e \ln n)$ fitness
evaluations. Since an offspring population size $\lambda$ of order $n \log n$
can prevent genetic drift, the UMDA can solve the DLB problem with $O(n^2 \log
n)$ fitness evaluations. In contrast, for classic evolutionary algorithms no
better run time guarantee than $O(n^3)$ is known (which we prove to be tight
for the ${(1+1)}$ EA), so our result rather suggests that the UMDA can cope
well with deception and epistatis.
  From a broader perspective, our result shows that the UMDA can cope better
with local optima than evolutionary algorithms; such a result was previously
known only for the compact genetic algorithm. Together with the lower bound of
Lehre and Nguyen, our result for the first time rigorously proves that running
EDAs in the regime with genetic drift can lead to drastic performance losses.
