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.

翻译：Lehre 和 Nguyen (FOGA 2019) 在最近的工作中, Lehre 和 Nguyen (FOGA 2019) 显示, 单亚利特边际分布算法(UMADA) 在母体人口规模上需要时间指数化的时间来优化欺骗性LeadingBlock (DLB) 问题。 他们从这一结果中得出结论, 单亚利弗te EDAs在欺骗和粘附上存在困难。 在这项工作中, 我们显示这一负面结论是由于对UMADA参数的选择很不幸。 当人口规模被选择大到足以防止遗传性漂移时, UMADA (UDA) 需要将DLB 问题优化为高概率, 最多为 $\ lambda (fradia{n= dreax), + 2 e nn 健康评价。 而对于常规进化算法来说, 通常不会比 $O (n3) 美元 优化。 。 。, 从我们证明 rolex liver liver lidealdealtradealdealde 和 EA1 的递化结果可以更接近于 。