Accepting Hybrid Networks of Evolutionary Processors are bio-inspired, massively parallel computing models that have been used succesfully in characterizing several usual complexity classes and also in solving efficiently decision problems. However, this model does not seem close to the usual algorithms, used in practice, since, in general, it lacks the property of stopping on every input. We add new features in order to construct a model that has this property, and also, is able to characterize uniformly CoNP, issue that was not solved in the framework of regular AHNEPs. This new model is called Timed AHNEPs (TAHNEP). We continue by adressing the topic of problem solving by means of this new defined model. Finally, we propose a tehnique that can be used in the design of algorithms as efficient as possible for a given problem; this tehnique consists in defining the notion of Problem Solver, a model that extends the previously defined TAHNEP.


Polynomial Time Turing Machine Output Node Problem Solver Input String 
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  1. 1.
    Castellanos, J., Martin-Vide, C., Mitrana, V., Sempere, J.: Solving NP-complete problems with networks of evolutionary processors. In: Mira, J., Prieto, A. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 621–628. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Castellanos, J., Martin-Vide, C., Mitrana, V., Sempere, J.: Networks of evolutionary processors. Acta Informatica 39, 517–529 (2003)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Cormen, T.H., Leiserson, C.E., Rivest, R.R.: Introduction to Algorithms. MIT Press, Cambridge (1990)zbMATHGoogle Scholar
  4. 4.
    Garey, M.R., Johnson, D.S.: Computers and Intractability. A Guide to the Theory of NP-Completness. W.H. Freeman, New York (1979)Google Scholar
  5. 5.
    Manea, F., Martin-Vide, C., Mitrana, V.: Solving 3CNF-SAT and HPP in Linear Time Using WWW. In: Margenstern, M. (ed.) MCU 2004. LNCS, vol. 3354, pp. 269–280. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Manea, F.: Using AHNEPs in the Recognition of Context-Free Languages. Grammars (in press); In: Proc. of the Workshop on Symbolic Networks, ECAI 2004 (2004)Google Scholar
  7. 7.
    Manea, F., Margenstern, M., Mitrana, V., Perez-Jimenez, M.: A new characterization for NP (submitted)Google Scholar
  8. 8.
    Margenstern, M., Mitrana, V., Perez-Jimenez, M.: Accepting hybrid networks of evolutionary processors. In: Pre-proc. DNA, vol. 10, pp. 107–117 (2004)Google Scholar
  9. 9.
    Martin-Vide, C., Mitrana, V., Perez-Jimenez, M., Sancho-Caparrini, F.: Hybrid networks of evolutionary processors. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 401–412. Springer, Heidelberg (2003)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Florin Manea
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania

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