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A Statistical Approach to the Identification of Diploid Cellular Automata

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Book cover Theory and Practice of Natural Computing (TPNC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10687))

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Abstract

In this paper, the identification problem of diploid Cellular Automata is considered, in which, based on a series of observations, the underlying cellular automaton rules are to be uncovered. A solution algorithm based on a statistical parameter estimation method using a normal distribution approximation is proposed. The accuracy of this method is verified in a series of computational experiments.

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, 01–447, Warsaw, Poland

    Witold Bołt

  2. Institute of Mathematics, Faculty of Mathematics, Physics and Informatics, University of Gdańsk, 80–308, Gdańsk, Poland

    Aleksander Bołt & Barbara Wolnik

  3. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, 9000, Ghent, Belgium

    Jan M. Baetens & Bernard De Baets

Authors
  1. Witold Bołt
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  2. Aleksander Bołt
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  3. Barbara Wolnik
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  4. Jan M. Baetens
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  5. Bernard De Baets
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Corresponding author

Correspondence to Witold Bołt .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. Academy of Sciences of the Czech Republic, Prague, Czech Republic

    Roman Neruda

  3. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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Bołt, W., Bołt, A., Wolnik, B., Baetens, J.M., De Baets, B. (2017). A Statistical Approach to the Identification of Diploid Cellular Automata. In: Martín-Vide, C., Neruda, R., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science(), vol 10687. Springer, Cham. https://doi.org/10.1007/978-3-319-71069-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-71069-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71068-6

  • Online ISBN: 978-3-319-71069-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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