Exploiting spatio-temporal data for the multiobjective optimization of cellular automata models

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.