Networks of evolutionary processors (NEP for short) form a class of models within the new computational paradigms inspired by biological phenomena. They are known to be theoretically capable of solving intractable problems. So far, there are two main categories that differ from each other by the nature of filtering process controlling the communication step: random-context clauses or polarization. Several studies have proven that both of them are computationally complete through efficient simulations of universal computational models such as Turing machines and 2-tag systems. Nevertheless, the indirect conversion between the two network variants results in an exponential increase of the computational complexity. In this paper, we suggest a direct simulation of polarized NEP through NEP with random-context filters which incurs in lower complexity costs.

进化处理器网络（简称NEP）在受生物学现象启发的新计算范例中形成了一类模型。从理论上讲，它们能够解决棘手的问题。到目前为止，由于控制通信步骤的过滤过程的性质，有两个主要类别彼此不同：随机上下文子句或极化。多项研究证明，通过对通用计算模型（例如，图灵机和2标签系统）进行有效的仿真，它们两者在计算上都是完整的。然而，两个网络变量之间的间接转换导致计算复杂性呈指数增长。在本文中，