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

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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References

  • Spataro, W., D’Ambrosio, D., Rongo, R., Trunfio, G.A.: An evolutionary approach for modelling lava flows through cellular automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 725–734. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  • Clarke, K., Hoppen, S., Gaydos, L.: A self-modifying cellular automaton model of historical urbanization in the San Francisco bay area. Environment and Planning B-Planning and Design 24, 247–261 (1997)

    Article  Google Scholar 

  • Yeh, A.G.O., Li, X.: Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. Journal of Geogr. Inf. Science 16, 323–343 (2002)

    Article  Google Scholar 

  • Li, X., Yeh, A.G.O.: Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science 18, 723–744 (2004)

    Article  Google Scholar 

  • Yeh, A.G.O., Li, X.: Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photog. Eng. & Remote Sensing 69, 1043–1052 (2003)

    Google Scholar 

  • Trunfio, G.A.: Enhancing cellular automata by an embedded generalized multilayer perceptron. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 343–348. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  • Goldstein, N.C.: Brains vs. brawn comparative strategies for the calibration of a cellular automata based urban growth model. In: Proceedings of the 7th International Conference on GeoComputation (2003)

    Google Scholar 

  • Pareto, V.: Cours d’Economie Politique. Volume I, II. F. Rouge, Lausanne (1896)

    Google Scholar 

  • Di Gregorio, S., Serra, R.: An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata. Future Generation Computer Systems 16, 259–271 (1999)

    Article  Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comp. 6, 182–197 (2002)

    Article  Google Scholar 

  • Project Gigalopolis, NCGIA (2003), http://www.ncgia.ucsb.edu/projects/gig/

  • Lee, D., Sallee, G.: A method of measuring shape. Geographical Review 60, 555–563 (1970)

    Article  Google Scholar 

  • Yang, X., Lo, C.: Modelling urban growth and landscape change in the Atlanta metropolitan area. International Journal of Geographical Information Science 17, 463–488 (2003)

    Article  Google Scholar 

  • Silva, E., Clarke, K.: Calibration of the sleuth urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems 26, 525–552 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Palazzo del Pou Salit, DAP – University of Sassari, Piazza Duomo, 6, I07041, Alghero (SS), Italy

    Giuseppe A. Trunfio

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  1. Giuseppe A. Trunfio
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Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Trunfio, G.A. (2006). Exploiting Spatio–temporal Data for the Multiobjective Optimization of Cellular Automata Models. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_10

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  • DOI: https://doi.org/10.1007/11875581_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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