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.
Evaluating cellular automata models by evolutionary multiobjective calibration
D'AMBROSIO, Donato;DI GREGORIO, Salvatore;RONGO, Rocco;Spataro W;
2008-01-01
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.File in questo prodotto:
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