The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.

遗传处理器网络 (NGP) 是基于对字符串的遗传操作的非常规计算模型，即在遗传算法中建立的变异和交叉操作。最初，它们被提议作为决策问题解决器的接受器机器。在这种情况下，已经证明它们是等同于图灵机的通用计算模型。在这项工作中，我们建议 NGP 作为枚举设备，并分析它们的计算能力。首先，我们定义模型并将其定义为并行遗传算法。一旦建立了两种形式主义之间的对应关系，我们就在形式语言理论的研究框架下对 NGP 的生成能力进行了研究。我们研究了模型的处理器数量与其生成能力之间的关系。我们的结果表明，处理器的数量对于将模型的生成能力提高到上限很重要，并且如果 NGP 被制定为生成设备，则它们是通用的计算模型。这使我们能够确认在某些限制下工作的并行遗传算法可以被认为等同于图灵机，因此，它们是计算的通用模型。