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Exploiting Spatio–temporal Data for the Multiobjective Optimization of Cellular Automata Models

  • Giuseppe A. Trunfio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

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.

Keywords

Cellular Automaton Multiobjective Optimization Cellular Automaton Cellular Automaton Model Cellular Automaton Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Giuseppe A. Trunfio
    • 1
  1. 1.Palazzo del Pou SalitDAP – University of SassariAlghero (SS)Italy

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