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Simulating polarization by random context filters in networks of evolutionary processors

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Abstract

Networks of evolutionary processors (NEP for short) form a class of models within the new computational paradigms inspired by biological phenomena. They are known to be theoretically capable of solving intractable problems. So far, there are two main categories that differ from each other by the nature of filtering process controlling the communication step: random-context clauses or polarization. Several studies have proven that both of them are computationally complete through efficient simulations of universal computational models such as Turing machines and 2-tag systems. Nevertheless, the indirect conversion between the two network variants results in an exponential increase of the computational complexity. In this paper, we suggest a direct simulation of polarized NEP through NEP with random-context filters which incurs in lower complexity costs.

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Authors and Affiliations

  1. Departamento de Sistemas Informáticos, Universidad Politécnica de Madrid, C/Alan Turing s/n, 28031, Madrid, Spain

    Victor Mitrana & Jose Angel Sanchez Martin

  2. National Institute for Research and Development of Biological Sciences, Independentei Bd. 296, 060031, Bucharest, Romania

    Victor Mitrana

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  1. Victor Mitrana
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  2. Jose Angel Sanchez Martin
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Correspondence to Victor Mitrana.

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This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI-UEFISCDI, Project No. PN-III-P2-2.1-PED-2019-2391, within PNCDI III.

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Mitrana, V., Sanchez Martin, J.A. Simulating polarization by random context filters in networks of evolutionary processors. J. Appl. Math. Comput. 68, 553–569 (2022). https://doi.org/10.1007/s12190-021-01542-9

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