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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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
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