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）的实际应用中，需要优化两个关键方面。首先，搜索方法的参数需针对具体领域进行调整，以有效平衡探索与利用。其次，搜索方法需采用分布式架构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，同时实现上述两个目标。BLADE使用“毯子”（即遗传表示上的掩码）在搜索过程中动态调整进化算子，并通过中心辐射式分布实现搜索过程。本文：（1）将毯式方法形式化为（1+1）EA情况下的马尔可夫链过程，通过分析随机矩阵的主特征值和次主特征值验证其有效性，并提出了可推广的理论；（2）利用适应度层理论分析分布式方法；（3）在三个基准问题上通过实验验证上述见解，证明毯式方法和分布式策略均能加速进化。此外，两者之间出现了令人惊讶的协同效应：当与分布式结合时，毯式方法在某些情况下使用n个客户端实现了超过n倍的加速。该工作突显了在实际应用中优化进化计算的重要性和潜力。