Accepting Networks of Genetic Processors are computationally complete

Accepting Networks of Genetic Processors are computationally complete

We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of Genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we ...

Networks of Polarized Evolutionary Processors Are Computationally Complete

In this paper, we consider the computational power of a new variant of networks of evolutionary processors which seems to be more suitable for a software and hardware implementation. Each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined, the data polarization is dynamically computed by mea...

Accepting networks of splicing processors: Complexity results

In this paper we consider a new, bio-inspired computing model: the accepting network of splicing processors. We define two computational complexity classes based on this model and show how they are related to the classical ones defined for Turing machines, namely NP and PSPACE. Furthermore, we approach the topic of problem solving using these newly defined devices. In this context, a linear tim...

Computationally complete chains of evolutionary processors with random context filters

Target Based Accepting Networks of Evolutionary Processors with Regular Filters

In this paper, a new definition of accepting networks – called target based accepting networks – is given. In a target based accepting network of evolutionary processors, each node is equipped with a regular language – the target set. As soon as a node contains a word of its target set, the input word is accepted by the network. In this way, no further output nodes are necessary. It is shown th...