All NP-Problems Can Be Solved in Polynomial Time by Accepting Networks of Splicing Processors of Constant Size

  • Florin Manea
  • Carlos Martín-Vide
  • Victor Mitrana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4287)


In this paper, we present two new results regarding ANSPs. The first one states that every recursively enumerable language can be accepted by an ANSP of size 7 out of which 6 do not depend on the given language. Then we propose a method for constructing, given an NP-language, an ANSP of size 7 accepting that language in polynomial time. Unlike the previous case, all nodes of this ANSP depend on the given language. Since each ANSP may be viewed as a problem solver as shown in [6], the later result may be interpreted as a method for solving every NP-problem in polynomial time by an ANSP of size 7.


Polynomial Time Turing Machine Mathematical Linguistics Communication Step Input Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Csuhaj-Varjú, E., Kari, L., Păun, G.: Test tube distributed systems based on splicing. Computers and AI 15, 211–232 (1996)MATHGoogle Scholar
  2. 2.
    Errico, L., Jesshope, C.: Towards a new architecture for symbolic processing. In: Artificial Intelligence and Information-Control Systems of Robots 1994, pp. 31–40. World Scientific Publishing, Singapore (1994)Google Scholar
  3. 3.
    Fahlman, S.E., Hinton, G.E., Seijnowski, T.J.: Massively parallel architectures for AI: NETL, THISTLE and Boltzmann machines. In: Proc. AAAI National Conf. on AI, pp. 109–113. William Kaufman, Los Altos (1983)Google Scholar
  4. 4.
    Garey, M., Johnson, D.: Computers and Intractability. A Guide to the Theory of NP-completeness. Freeman, New York (1979)MATHGoogle Scholar
  5. 5.
    Hillis, W.D.: The Connection Machine. MIT Press, Cambridge (1985)Google Scholar
  6. 6.
    Manea, F., Martín-Vide, C., Mitrana, V.: Accepting networks of splicing processors: complexity results. Theoretical Computer Science (to appear)Google Scholar
  7. 7.
    Martín-Vide, C., Mitrana, V.: Networks of evolutionary processors: results and perspectives. In: Molecular Computational Models: Unconventional Approaches, pp. 78–114. Idea Group Publishing, Hershey (2005)Google Scholar
  8. 8.
    Păun, G., Rozenberg, G., Salomaa, A.: DNA Computing. New Computing Paradigms. Springer, Berlin (1998)MATHGoogle Scholar
  9. 9.
    Păun, G.: Distributed architectures in DNA computing based on splicing: Limiting the size of components. In: Unconventional Models of Computation, pp. 323–335. Springer, Berlin (1998)Google Scholar
  10. 10.
    Sankoff, D., et al.: Gene order comparisons for phylogenetic inference:Evolution of the mitochondrial genome. Proc. Natl. Acad. Sci. USA 89, 6575–6579 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Florin Manea
    • 1
  • Carlos Martín-Vide
    • 2
  • Victor Mitrana
    • 1
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania
  2. 2.Research Group in Mathematical LinguisticsRovira i Virgili UniversityTarragonaSpain

Personalised recommendations