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Small universal accepting hybrid networks of evolutionary processors

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Abstract

In this paper, we improve some results regarding the size complexity of accepting hybrid networks of evolutionary processors (AHNEPs). We show that there are universal AHNEPs of size 6, by devising a method for simulating 2-tag systems. This result improves the best upper bound for the size of universal AHNEPs which was 7. We also propose a computationally and descriptionally efficient simulation of nondeterministic Turing machines with AHNEPs. More precisely, we prove that AHNEPs with ten nodes can simulate any nondeterministic Turing machine of time complexity f (n) in time O(f (n)). This result significantly improves the best known upper bound for the number of nodes in a network simulating in linear time an arbitrary Turing machine, namely 24.

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Authors and Affiliations

  1. EMBL, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

    Remco Loos

  2. Faculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014, Bucharest, Romania

    Florin Manea & Victor Mitrana

  3. Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, PSF 4120, 39016, Magdeburg, Germany

    Florin Manea

  4. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Victor Mitrana

Authors
  1. Remco Loos
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  2. Florin Manea
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  3. Victor Mitrana
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Corresponding author

Correspondence to Victor Mitrana.

Additional information

Work is supported by the research grant ES-2006-0146 of the Spanish Ministry of Science and Innovation and the Romanian Ministry of Education and Research (PN-II Program, Projects GlobalComp, SEFIN and SELF). The work of Florin Manea is also supported by the Alexander von Humboldt Foundation.

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Loos, R., Manea, F. & Mitrana, V. Small universal accepting hybrid networks of evolutionary processors. Acta Informatica 47, 133–146 (2010). https://doi.org/10.1007/s00236-009-0113-8

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  • DOI: https://doi.org/10.1007/s00236-009-0113-8

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