Networks of evolutionary processors with a self-organizing learning

This work is a continuation of the research started in Castellanos, J. et al. (2003) and Varju, E.C. et al. (2000) where one has considered a mechanism inspired from cell biology, namely networks of evolutionary processors, that is networks whose nodes are very simple processors able to perform just one type of point mutation (insertion, deletion or substitution of a symbol). These nodes are endowed with filters which are defined by some membership or random context condition. Each processor placed in a node is a very simple processor, an evolutionary processor. By an evolutionary processor we mean a processor which is able to perform very simple operations, namely point mutations in a DNA sequence (insertion, deletion or substitution of a pair of nucleotides). More generally, each node may be viewed as a cell having a genetic information encoded in DNA sequences which may evolve by local evolutionary events, that is point mutations. Each node is specialized just for one of these evolutionary operations. Furthermore, the data in each node is organized in the form of multisets of strings, each copy being processed in parallel such that all the possible evolutions events that can take place do actually take place. These networks may be used as language (macroset) generating devices or as computational ones. Here, we consider them as computational mechanisms and show how an NP-complete problem can be solved in linear time. It is worth mentioning here the similarity of this model to that of a P system, a new computing model inspired by the hierarchical and modularized cell structure recently proposed in Paun, G. (2000) and Paun, G. et al. (1998). Networks of evolutionary processors (NEP) Castellanos, J. et al. (2003) and Varju, E.C. et al. (2000) are language generating device, if we look at the strings collected in the output node. We can also look at them as doing some computation. If we consider these networks with nodes having filters defined by random context conditions, which seems to be closer to the recent possibilities of biological implementation, then using these simple mechanisms we can solve NP-complete problems in linear time.