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The Density Classification Problem for Multi-states Cellular Automata

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Advances in Artificial Life (ECAL 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3630))

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Abstract

In this paper, the results of three experiments, in which a genetic algorithm evolves one-dimensional cellular automata (CA), in order to perform the classical main task, are reported. The used systems are not elementary CA but they have a higher number of states. Our aim is to verify if the main-task results are similar to those obtained with elementary CA. Our results confirm that there is a substantial homogeneity.

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Author information

Authors and Affiliations

  1. Università della Calabria, 87036, Arcavacata di Rende, (CS), Italy

    Anna Rosa Gabriele

Authors
  1. Anna Rosa Gabriele
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Editor information

Editors and Affiliations

  1. Natural Computation Group, University of Kent, CT2 7NF, Canterbury, UK

    Mathieu S. Capcarrère

  2. Computing Laboratory and Centre for BioMedical Informatics, University of Kent, CT2 7NF, Canterbury, UK

    Alex A. Freitas

  3. Computer Science Department, University College London, London, UK

    Peter J. Bentley

  4. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  5. Department of Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jon Timmis

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© 2005 Springer-Verlag Berlin Heidelberg

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Gabriele, A.R. (2005). The Density Classification Problem for Multi-states Cellular Automata. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_45

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  • DOI: https://doi.org/10.1007/11553090_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28848-0

  • Online ISBN: 978-3-540-31816-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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