In this paper we consider a new variant of Networks of Polarized Evolutionary Processors (NPEP) named Generalized Networks of Evolutionary Polarized Processors (GNPEP) and propose them as solvers of combinatorial optimization problems. Unlike the NPEP model, GNPEP uses its numerical evaluation over the processed data from a quantitative perspective, hence this model might be more suitable to solve specific hard problems in a more efficient and economic way. In particular, we propose a GNPEP network to solve a well-known NP-hard problem, namely the [Formula: see text]-queens. We prove that this GNPEP algorithm requires a linear time in the size of a given instance. This result suggests that the GNPEP model is more suitable to address problems related to combinatorial optimization in which integer restrictions have a relevant role.

中文翻译：

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使用进化极化处理器的广义网络的 N-Queens 问题的线性时间解

在本文中，我们考虑了一种新的极化进化处理器网络 (NPEP) 变体，称为进化极化处理器的广义网络 (GNPEP)，并建议将它们作为组合优化问题的求解器。与 NPEP 模型不同，GNPEP 从定量的角度对处理后的数据进行数值评估，因此该模型可能更适合以更有效和经济的方式解决特定的难题。特别是，我们提出了一个 GNPEP 网络来解决一个众所周知的 NP-hard 问题，即 [Formula: see text]-queens。我们证明了这个 GNPEP 算法在给定实例的大小上需要一个线性时间。这一结果表明，GNPEP 模型更适合解决与组合优化相关的问题，其中整数限制具有相关作用。