Complexity-preserving simulations among three variants of accepting networks of evolutionary processors
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In this paper we consider three variants of accepting networks of evolutionary processors. It is known that two of them are equivalent to Turing machines. We propose here a direct simulation of one device by the other. Each computational step in one model is simulated in a constant number of computational steps in the other one while a translation via Turing machines squares the time complexity. We also discuss the possibility of constructing simulations that preserve not only complexity, but also the shape of the simulated network.
KeywordsEvolutionary processor Uniform evolutionary processor Network of evolutionary processors Filtered connection
This work was supported by the Academy of Finland, projects 132727, 122426, and 108421. F. Manea acknowledges the support from the Alexander von Humboldt Foundation. J Sempere acknowledges the support from the Spanish Ministerio de Educación y Ciencia project TIN2007-60769.
- Alhazov A, Bel Enguix G, Rogozhin Y (2009a) Obligatory hybridnetworks of evolutionary processors. In: International conference on agents and artificial intelligence (ICAART 2009), pp 613–618Google Scholar
- Bottoni P, Labella A, Manea F, Mitrana V, Sempere J (2009a) Filter position in networks of evolutionary processors does not matter: a direct proof. In: Proc. 15th international meeting on DNA computing and molecular programming. 8–11 June 2009, Fayetteville, ArkansasGoogle Scholar
- Bottoni P, Labella A, Mitrana V, Sempere JM (2009b) Networks of evolutionary picture processors with filtered connections. In: Unconventional computation, 8th international conference (UC 2009), LNCS, vol 5715. Springer, Heidelberg, pp 70–84 Google Scholar
- Castellanos J, Martín-Vide C, Mitrana V, Sempere J (2001) Solving NP-complete problems with networks of evolutionary processors. In: International work-conference on artificial and natural neural networks (IWANN 2001), Lecture notes in computer science, vol 2084, pp 621–628Google Scholar
- Csuhaj-Varjú E, Salomaa A (1997) Networks of parallel language processors. In: New trends in formal languages, Lecture notes in computer science, vol 1218, pp 299–318Google Scholar
- Dassow J, Truthe B (2007) On the power of networks of evolutionary processors. In: Machines, computations, and universality (MCU 2007), Lecture notes in computer science, vol 4667, pp 158–169Google Scholar
- Errico L, Jesshope C (1994) Towards a new architecture for symbolic processing. In: Artificial intelligence and information-control systems of robots ’94, World Scientific, Singapore, pp 31–40Google Scholar
- Fahlman SE, Hinton GE, Seijnowski TJ (1983) Massively parallel architectures for AI: NETL, THISTLE and Boltzmann machines. In: Proc. of the national conference on artificial intelligence, pp 109–113Google Scholar
- Hillis W (1985) The connection machine. MIT Press, CambridgeGoogle Scholar
- Margenstern M, Mitrana V, Perez-Jimenez M (2005) Accepting hybrid networks of evolutionary systems. In: DNA based computers 10, Lecture notes in computer science, vol, pp 235–246Google Scholar
- Martín-Vide C, Mitrana V (2005) Networks of evolutionary processors: results and perspectives. In: Molecular computational models: unconventional approaches. dea Group Publishing, Hershey, pp 78–114Google Scholar