In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in order to balance exploration and exploitation effectively. Second, the search method needs to be distributed to take advantage of parallel computing resources. This paper presents BLADE (BLAnket Distributed Evolution) as an approach to achieving both goals simultaneously. BLADE uses blankets (i.e., masks on the genetic representation) to tune the evolutionary operators during the search, and implements the search through hub-and-spoke distribution. In the paper, (1) the blanket method is formalized for the (1 + 1)EA case as a Markov chain process. Its effectiveness is then demonstrated by analyzing dominant and subdominant eigenvalues of stochastic matrices, suggesting a generalizable theory; (2) the fitness-level theory is used to analyze the distribution method; and (3) these insights are verified experimentally on three benchmark problems, showing that both blankets and distribution lead to accelerated evolution. Moreover, a surprising synergy emerges between them: When combined with distribution, the blanket approach achieves more than $n$-fold speedup with $n$ clients in some cases. The work thus highlights the importance and potential of optimizing evolutionary computation in practical applications.

翻译：在构建应用遗传算法的实用应用中，有两个关键的优化：第一，需要将搜索方法的参数调整到特定领域的状态，以实现有效的探索和开发的平衡；第二，需要将搜索方法分布式处理以利用并行计算资源。本文提出了BLADE（BLAnket Distributed Evolution）作为一种同时实现两个目标的方法。BLADE使用盖子(即遗传表示上的蒙版)来调整搜索中的进化算子，并通过中心与集线器的分布式方法实现搜索。本文（1）针对（1+1）EA情况下正式化盖子方法，作为马尔可夫链过程，并通过分析随机矩阵的占优和次占优特征值来验证其有效性，从而提出了可推广的理论；（2）使用适用水平理论分析了分发方法；（3）在三个基准问题上进行实验验证，展示了盖子和分布式方法带来的进化加速效果。此外，两者之间出现了意想不到的协同效应：在某些情况下，结合分布，盖子方法在n个客户端的情况下实现了超过n倍的加速。该研究凸显了在实用应用中优化进化算法的重要性和潜力。