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Evaluating Cellular Automata Models by Evolutionary Multiobjective Calibration

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Cellular Automata (ACRI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5191))

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Abstract

This paper proposes a multi-objective approach for Cellular Automata (CA) calibration. The method exploits the available temporal sequences of spatial data in order to produce CAs which are non-dominated (i.e. Pareto optimal) with respect to multiple objectives representing the disagreement between the simulated and real dynamics. A preliminary application, based on a parallel multi-objective Genetic Algorithm, showed that the proposed approach can provide significant insights about potentialities and limits of a CA model.

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Authors and Affiliations

  1. Department of Earth Sciences, University of Calabria, 87036, Rende (CS), Italy

    Maria Vittoria Avolio, Valeria Lupiano & Rocco Rongo

  2. Department of Mathematics, University of Calabria, 87036, Rende (CS), Italy

    Donato D’Ambrosio, Salvatore Di Gregorio & William Spataro

  3. DAP, University of Sassari, 07041, Alghero (SS), Italy

    Giuseppe A. Trunfio

Authors
  1. Maria Vittoria Avolio
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  2. Donato D’Ambrosio
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  3. Salvatore Di Gregorio
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  4. Valeria Lupiano
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  5. Rocco Rongo
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  6. William Spataro
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  7. Giuseppe A. Trunfio
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Editor information

Hiroshi Umeo Shin Morishita Katsuhiro Nishinari Toshihiko Komatsuzaki Stefania Bandini

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© 2008 Springer-Verlag Berlin Heidelberg

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Avolio, M.V. et al. (2008). Evaluating Cellular Automata Models by Evolutionary Multiobjective Calibration. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds) Cellular Automata. ACRI 2008. Lecture Notes in Computer Science, vol 5191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79992-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-79992-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79991-7

  • Online ISBN: 978-3-540-79992-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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