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Discovery by Genetic Algorithm of Cellular Automata Rules for Pattern Reconstruction Task

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Cellular Automata (ACRI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6350))

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Abstract

This paper presents results of the study on application of two-dimensional, three-state cellular automata with von Neumann neighborhood to perform pattern reconstruction task. Searching efficient cellular automata rules is conducted with use of a genetic algorithm. Experiments show a very good performance of discovered rules in solving the reconstruction task despite minimum radius of neighborhood and only partial knowledge about neighborhood states available. The paper also presents interesting reusability possibilities of discovered rules in reconstructing patterns different but similar to ones used during artificial evolution.

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References

  1. Bandini, S., Vanneschi, L., Wuensche, A., Shehata, A.B.: A Neuro-Genetic Framework for Pattern Recognition in Complex Systems. Fundamenta Informaticae 87(2), 207–226 (2008)

    MathSciNet  MATH  Google Scholar 

  2. Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Processing Magazine 14, 24–41 (1997)

    Article  Google Scholar 

  3. Breukelaar, R., Back, T.: Evolving Transition Rules for Multi Dimensional Cellular Automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 182–191. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Das, R., Crutchfield, J.P., Mitchell, M.: Evolving globally synchronized cellular automata. In: Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  5. Fawcett, T.: Data mining with cellular automata. ACM SIGKDD Explorations Newsletter 10(1), 32–39 (2008)

    Article  Google Scholar 

  6. Hernandez, G., Herrmann, H.: Cellular automata for elementary image enhancement. Graphical Models and Image Processing 58(1), 82–89 (1996)

    Article  Google Scholar 

  7. Mitchell, M., Hraber, P.T., Crutchfield, J.P.: Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations. Complex Systems 7, 89–130 (1993)

    MATH  Google Scholar 

  8. Rosin, P.L.: Training Cellular Automata for Image Processing. IEEE Transactions on Image Processing 15(7), 2076–2087 (2006)

    Article  Google Scholar 

  9. Slatnia, S., Batouche, M., Melkemi, K.E.: Evolutionary Cellular Automata Based-Approach for Edge Detection. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 404–411. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Swiecicka, A., Seredynski, F., Zomaya, A.Y.: Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support. IEEE Transactions on Parallel and Distributed Systems 17(3), 253–262 (2006)

    Article  Google Scholar 

  11. Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Faculty, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Anna Piwonska

  2. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Franciszek Seredynski

  3. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Franciszek Seredynski

Authors
  1. Anna Piwonska
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  2. Franciszek Seredynski
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Editor information

Editors and Affiliations

  1. Complex Systems and Artificial Intelligence Research Center, University of Milano-Bicocca, Viale Sarca 336, Milano, Italy

    Stefania Bandini , Sara Manzoni  & Giuseppe Vizzari ,  & 

  2. University of Osaka Electro-Communication, 572-8530, Neyagawa, Osaka, Japan

    Hiroshi Umeo

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© 2010 Springer-Verlag Berlin Heidelberg

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Piwonska, A., Seredynski, F. (2010). Discovery by Genetic Algorithm of Cellular Automata Rules for Pattern Reconstruction Task. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds) Cellular Automata. ACRI 2010. Lecture Notes in Computer Science, vol 6350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15979-4_22

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  • DOI: https://doi.org/10.1007/978-3-642-15979-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15978-7

  • Online ISBN: 978-3-642-15979-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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