Optimal Performance: Underlying Octahedron Graph of Evolutionary Processors

Networks of evolutionary processors with an underlying octahedron graph consist of 7 language processors which are linked to the vertices of the octahedron graph. Notice that they are located in the 6 facets and the core of a cube graph. Also note that the nodes are only able to perform a type of mutation based on the words found in that node. Each node is associated with an input filter and an output filter, defined by some regular language. Rules are applied to all the words existing in every node. The words, able to pass the output filter of the respective node, are sent out and they navigate through the graph. Such words will enter those nodes provided their input filters are satisfied. The computational power of the network is comparable to Turing machines when the filters are regular languages. We introduce several variants of octahedron networks, depending on rule types and the way of computation plus their computational power. Some known problems are addressed at the end.

[1]  Victor Mitrana,et al.  Evolutionary systems: a language generating device inspired by evolving communities of cells , 2000, Acta Informatica.

[2]  Gheorghe Paun,et al.  At the crossroads of DNA computing and formal languages: Characterizing recursively enumerable languages using insertion-deletion systems , 1997, DNA Based Computers.

[3]  Victor Mitrana,et al.  Networks of evolutionary processors , 2003, Acta Informatica.

[4]  Victor Mitrana,et al.  Splicing Grammar Systems , 1996, Comput. Artif. Intell..

[5]  Fernando Arroyo,et al.  A HIERARCHICAL ARCHITECTURE WITH PARALLEL COMUNICATION FOR IMPLEMENTING P SYSTEMS , 2008 .

[6]  Artiom Alhazov,et al.  On the number of nodes in universal networks of evolutionary processors , 2006, Acta Informatica.

[7]  Santiago Alonso Villaverde,et al.  A Circuit Implementing Massive Parallelism in Transition P Systems. , 2008 .

[8]  Carlos Martín-Vide,et al.  Characterizations of Recursively Enumerable Languages by Means of Insertion Grammars , 1998, Theor. Comput. Sci..

[9]  Victor Mitrana,et al.  Solving NP-Complete Problems With Networks of Evolutionary Processors , 2001, IWANN.

[10]  Artiom Alhazov,et al.  Networks of Evolutionary Processors with Two Nodes Are Unpredictable , 2007, LATA.

[11]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[12]  Nivat G. Päun,et al.  Handbook of Formal Languages , 2013, Springer Berlin Heidelberg.

[13]  Artiom Alhazov,et al.  On Networks of Evolutionary Processors with Nodes of Two Types , 2009, Fundam. Informaticae.

[14]  Victor Mitrana,et al.  Hybrid networks of evolutionary processors are computationally complete , 2004, Acta Informatica.

[15]  Victor Mitrana,et al.  Accepting Hybrid Networks of Evolutionary Processors , 2004, DNA.

[16]  Gheorghe Paun,et al.  DNA Computing: New Computing Paradigms , 1998 .

[17]  Viliam Geffert Normal forms for phrase-structure grammars , 1991, RAIRO Theor. Informatics Appl..