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：在构建进化计算（EC）的实际应用时，需要优化两个关键环节。其一，需根据领域特性调校搜索方法的参数，以有效平衡探索与利用的关系；其二，需对搜索方法进行分布式部署，以充分利用并行计算资源。本文提出BLADE（BLAnket Distributed Evolution，毯式分布式进化）方法，可同时实现上述两大目标。BLADE利用“毯式结构”（即遗传表示上的掩码）在搜索过程中调整进化算子，并通过中心辐射式分布式架构实施搜索。论文中：（1）将毯式方法形式化为(1+1)EA情形下的马尔可夫链过程，通过分析随机矩阵的主特征值与次特征值验证其有效性，并提出具有普适性的理论框架；（2）利用适应度层级理论分析分布式方法的特性；（3）在三个基准问题上进行实验验证，结果表明毯式结构与分布式方法均能加速进化。更令人惊喜的是，两者之间展现出协同效应：当与分布式方法结合时，毯式方法在某些情况下可实现n个客户端产生超过n倍的加速比。本工作凸显了在实际应用中优化进化计算的重要性与潜力。