Trunfio, Giuseppe Andrea (2006) Exploiting spatio-temporal data for the multiobjective optimization of Cellular Automata models. In: Intelligent Data Engineering and Automated Learning, IDEAL 2006: 7th International Conference: proceedings, September 20-23, 2006, Burgos, Spain. Berlin - New York, Springer. p. 81-89. (Lecture notes in computer science, 4224/2006). ISBN 978-3-540-45485-4. Conference or Workshop Item.
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The increased availability of remotely sensed spatio-temporal data offers the chance to improve the reliability of an important class of Cellular Automata (CA) models used for the simulation of real complex systems. To this end, this paper proposes a multiobjective approach, based on a genetic algorithm, which can present some significant advantages if compared with standard single-objective optimizations. The method exploits the available temporal sequences of spatial data in order to produce CAs which are non-dominated with respect to multiple objectives. The latter represent, in different metrics, the level of agreement between the simulated and real spatio-temporal processes. The set of non-dominated CAs proves to be a valuable source of information about potentialities and limits of a specific CA model structure.
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