Abstract
The automatic optimization of Cellular Automata (CA) models often requires a large number of time-consuming simulations before an acceptable solution can be found. As a result, CA optimization processes may involve significant computational resources. In this paper we investigate the possibility of speeding up a CA calibration through the approach of meta-model assisted search, which is widely used in many fields. The adopted technique relies on inexpensive surrogate functions able to approximate the fitness corresponding to the CA simulations. The calibration exercise presented here refers to SCIARA, a CA for the simulation of lava flows. According to the preliminary results, the use of meta-models enables to achieve a significant gain in computational time.
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References
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)
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)
Blecic, I., Cecchini, A., Trunfio, G.A.: A Comparison of Evolutionary Algorithms for Automatic Calibration of Constrained Cellular Automata. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010, Part I. LNCS, vol. 6016, pp. 166–181. Springer, Heidelberg (2010)
Crisci, G.M., Rongo, R., Gregorio, S.D., Spataro, W.: The simulation model SCIARA: the 1991 and 2001 lava flows at Mount Etna. Journal of Volcanology and Geothermal Research 132, 253–267 (2004)
Spataro, W., Avolio, M.V., Lupiano, V., Trunfio, G.A., Rongo, R., D’Ambrosio, D.: The latest release of the lava flows simulation model sciara: First application to Mt Etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface. Procedia CS 1, 17–26 (2010)
Willmes, L., Bäck, T., Jin, Y., Sendhoff, B.: Comparing neural networks and kriging for fitness approximation in evolutionary optimization. In: IEEE Congress on Evolutionary Computation, vol. (1), pp. 663–670 (2003)
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9, 3–12 (2005)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Morgan, N., Bourlard, H.: Advances in neural information processing systems 2, pp. 630–637. Morgan Kaufmann Publishers Inc., San Francisco (1990)
Nadaraya, E.: On estimating regression. Theory of Probability and Its Applications 9 (1964)
Watson, G.: Smooth regression analysis. Sankhya Series A, 359–372 (1969)
Specht, D.F.: A general regression neural network. IEEE Transactions on Neural Networks 2, 568–576 (1991)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley Professional (1989)
Liang, K.H., Yao, X., Newton, C.: Evolutionary search of approximated n-dimensional landscapes. International Journal of Knowledge-based Intelligent Engineering Systems 4, 172–183 (2000)
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D’Ambrosio, D., Rongo, R., Spataro, W., Trunfio, G.A. (2012). Meta-model Assisted Evolutionary Optimization of Cellular Automata: An Application to the SCIARA Model. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31500-8_55
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DOI: https://doi.org/10.1007/978-3-642-31500-8_55
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