Hairpin lengthening: algorithmic results.

We consider here a new variant of the hairpin completion, called hairpin lengthening, which seems more appropriate for practical implementation. The variant considered here concerns the lengthening of the word that forms a hairpin structure, such that this structure is preserved, without necessarily completing the hairpin. Although our motivation is based on biological phenomena, the present paper is more about some algorithmic properties of this operation. Finally, we propose an algorithm for computing the hairpin lengthening distance between two words in quadratic time

Uniform Distributed Pushdown Automata Systems.

We consider here uniform distributed pushdown automata systems (UDPAS), namely distributed pushdown automata systems having all components identical pushdown automata. We consider here just a single protocol for activating/deactivating components, namely a component stays active as long as it can perform moves, as well as two ways of accepting the input word: by empty stacks (all components have empty stacks) or by final states (all components are in final states), when the input word is completely read. We mainly investigate the computational power of UDPAS accepting by empty stacks and a few decidability and closure properties of the families of languages they define. Some directions for further work and open problems are also discussed

Networks of Polarized Evolutionary Processors as Problem Solvers.

In this paper, we propose a solution to an NP-complete problem, namely the "3-colorability problem", based on a network of polarized processors. Our solution is uniform and time efficient

Accepting Hybrid Networks of Evolutionary Processors

We consider time complexity classes defined on accepting hybrid networks of evolutionary processors (AHNEP) similarly to the classical time complexity classes defined on the standard computing model of Turing machine. By definition, AHNEPs are deterministic. We prove that the classical complexity class NP equals the set of languages accepted by AHNEPs in polynomial time

A New Characterization of NP, P, and PSPACE with Accepting Hybrid Networks of Evolutionary Processors

We consider three complexity classes defined on Accepting Hybrid Networks of Evolutionary Processors (AHNEP) and compare them with the classical complexity classes defined on the standard computing model of Turing machine. By definition, AHNEPs are deterministic. We prove that the classical complexity class NP equals the family of languages decided by AHNEPs in polynomial time. A language is in P if and only if it is decided by an AHNEP in polynomial time and space. We also show that PSPACE equals the family of languages decided by AHNEPs in polynomial length

Hybrid Networks of Evolutionary Processors

A hybrid network of evolutionary processors consists of several processors which are placed in nodes of a virtual graph and can perform one simple operation only on the words existing in that node in accordance with some strategies. Then the words which can pass the output filter of each node navigate simultaneously through the network and enter those nodes whose input filter was passed. We prove that these networks with filters defined by simple random-context conditions, used as language generating devices, are able to generate all linear languages in a very efficient way, as well as non-context-free languages. Then, when using them as computing devices, we present two linear solutions of the Common Algorithmic Problem.Ministerio de Ciencia y Tecnología TIC2002-04220-C03-0

Polarization: a new communication protocol in networks of bio-inspired processors

This work is a survey of the most recent results regarding the computational power of the networks of bio-inspired processors whose communication is based on a new protocol called polarization. In the former models, the communication amongst processors is based on filters defined by some random-context conditions, namely the presence of some symbols and the absence of other symbols. In the new protocol discussed here, a polarization (negative, neutral, and positive) is associated with each node, while the polarization of data navigating through the network is computed in a dynamical way by means of a valuation function. Consequently, the protocol of communication amongst processors is naturally based on the compatibility between their polarization and the polarization of the data. We consider here three types of bio-inspired processors: evolutionary processors, splicing processors, and multiset processors. A quantitative generalization of polarization (evaluation sets) is also presented. We recall results regarding the computational power of these networks considered as accepting devices. Furthermore, a solution to an intractable problem, namely the 0 / 1 Knapsack problem, based on the networks of splicing processors with evaluation sets considered as problem solving devices, is also recalled. Finally, we discuss some open problems and possible directions for further research in this area

Networks of Bio-inspired Processors

[EN] The goal of this work is twofold. Firstly, we propose a uniform view of three types of accepting networks of bio-inspired processors: networks of evolutionary processors, networks of splicing processors and networks of genetic processors. And, secondly, we survey some features of these networks: computational power, computational and descriptional complexity, the existence of universal networks, eciency as problem solvers and the relationships among them.Work partially supported by the Spanish Ministry of Science and Innovation under coordinated research project TIN2011-28260-C03-00 and research projects TIN2011-28260-C03-01, TIN2011-28260-C03-02 and TIN2011-28260-C03-03Arroyo Montoro, F.; Castellanos, J.; Mitrana, V.; Santos, E.; Sempere Luna, JM. (2012). Networks of Bio-inspired Processors. Triangle. 7:3-22. https://doi.org/10.17345/triangle7.3-22S322

Filter position in networks of substitution processors does not matter

Electronic version of an article published as International Journal of Foundations of Computer Science, Vol. 22, No. 1 (2011) 155–165, DOI: 10.1142/S0129054111007915] © [copyright World Scientific Publishing Company []http://www.worldscientific.com]It is known ([4]) that moving the filters from the nodes to the edges in accepting hybrid networks of evolutionary processors does not decrease the computational power of the model which equals that of a Turing machine. A direct and time complexity preserving simulation is presented in [1]. All three types of processors (substitution, insertion, deletion) are essentially used in this simulation. In this note we prove that such a direct simulation between networks containing substitution nodes only still exists. © 2011 World Scientific Publishing Company.Arroyo Montoro, F.; Castellanos, J.; Mitrana, V.; Santos, E.; Sempere Luna, JM. (2011). Filter position in networks of substitution processors does not matter. International Journal of Foundations of Computer Science. 22(1):155-165. doi:10.1142/S0129054111007915S155165221Csuhaj-Varjú, E., & Salomaa, A. (1997). Networks of parallel language processors. Lecture Notes in Computer Science, 299-318. doi:10.1007/3-540-62844-4_22Csuhaj-Varjú, E., & Mitrana, V. (2000). Evolutionary systems: a language generating device inspired by evolving communities of cells. Acta Informatica, 36(11), 913-926. doi:10.1007/s002360050178L. Errico and C. Jesshope, Artificial Intelligence and Information-Control Systems of Robots 94 (World Scientific, 1994) pp. 31–40.Manea, F., Margenstern, M., Mitrana, V., & Pérez-Jiménez, M. J. (2008). A New Characterization of NP, P, and PSPACE with Accepting Hybrid Networks of Evolutionary Processors. Theory of Computing Systems, 46(2), 174-192. doi:10.1007/s00224-008-9124-zF. Manea, C. Martin-Vide and V. Mitrana, Scientific Applications of Language Methods (World Scientific, 2010) pp. 523–560.Păun, G. (2000). Computing with Membranes. Journal of Computer and System Sciences, 61(1), 108-143. doi:10.1006/jcss.1999.169