Abstract
This work introduces SymbPar, a parallel meta-evolutionary algorithm designed to build Radial Basis Function Networks minimizing the number of parameters needed to be set by hand. Parallelization is implemented using independent agents to evaluate every individual. Experiments over classifications problems show that the new method drastically reduces the time took by sequential algorithms, while maintaining the generalization capabilities and sizes of the nets it builds.
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References
Alba, E., Luque, G.: Evaluation of Parallel Metaheuristics. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 9–14. Springer, Heidelberg (2006)
Bethke, A.D.: Comparison of genetic algorithms and gradient-based optimizers on parallel processors: Efficiency of use of processing capacity. Tech. Rep., University of Michigan, Ann Arbor, Logic of Computers Group (1976)
Castillo, P.A., Merelo, J.J., Rivas, V., Romero, G., Prieto, A.: G-Prop: Global Optimization of Multilayer Perceptrons using GAs. Neurocomputing 35/1-4, 149–163 (2000)
Castillo, P.A., Arenas, M.G., Merelo, J.J., Rivas, V., Romero, G.: Optimisation of Multilayer Perceptrons Using a Distributed Evolutionary Algorithm with SOAP. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 676–685. Springer, Heidelberg (2002)
Cortez, P., Rocha, M., Neves, J.: A meta-genetic algorithm for time series forecasting. In: Torgo, L. (ed.) Proceedings of Workshop on Artificial Intelligence Techniques for Financial Time Series Analysis (AIFTSA 2001), 10th Portuguese Conference on Artificial Intelligence (EPIA 2001), Oporto, Portugal, pp. 21–31 (2001)
Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern 16(1), 122–128 (1986)
Hernandez, A.I., Carrault, G., Mora, F., Bardou, A.: Model-based interpretation of cardiac beats by evolutionary algorithms: signal and model interaction. Artificial Intelligence in Medicine 26(3), 211–235 (2002)
Jelasity, M., Preub, M., Paechter, B.: A scalable and robust framework for distributed application. In Proc. on Evolutionary Computation, pp. 1540–1545 (2002).
Karp, A.H., Flatt, H.P.: Measuring parallel processor performance. Communications of the ACM 33(5), 539–543 (1990)
Laoutaris, N., Syntila, S., Stavrakakis, I.: Meta algorithms for Hierarchical Web Caches. In: IEEE International Conference on Performance, Computing, and Communications, pp. 445–452 (2004) ISBN: 0-7803-8396-6
Mercer, R.E., Sampson, J.R.: Adaptive search using a reproductive meta-plan. Kybernetes 7(3), 215–228 (1978)
Parras-Gutiérrez, E., Rivas, V.M., Merelo Juan, J., del Jesus, M.J.: Study of the robustness of a meta-algorithm for the estimation of parameters in Artificial Neural Networks design. In: HIS 2008: 8th International Conference on Hybrid Intelligent Systems, Barcelona, pp. 519–524. IEEE computer society, Los Alamitos (2008)
Rex, D.E., Shattuck, D.W., Woods, R.P., Narr, K.L., Luders, E., Rehm, K., Stolzner, S.E., Rottenberg, D.A., Toga, A.W.: A meta-algorithm for brain extraction in MRI. NeuroImage 23(2), 625–637 (2004)
Rivas, V.M., Merelo, J.J., Castillo, P.A., Arenas, M.G., Castellanos, J.G.: Evolving RBF neural networks for time-series forecasting with EvRBF. Information Sciences 165(3-4), 207–220 (2004)
Samsonovich, A.V., De Jong, K.A.: Pricing the ’free lunch’ of meta-evolution. In: Beyer, H.-G., O’Reilly, U.-M. (eds.) GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 1355–1362. ACM Press, New York (2005)
Shahookar, K., Mazumder, P.: VLSI cell placement techniques. ACM Comput. Surv. 23(2), 143–220 (1991)
Tomassini, M.: Parallel and distributed evolutionary algorithms: A review. In: Miettinen, K., et al. (eds.) Evolutionary Algorithms in Engineering and Computer Science, pp. 113–133. J. Wiley and Sons, Chichester (1999)
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Arenas, M.G., Parras-Gutiérrez, E., Rivas, V.M., Castillo, P.A., Del Jesus, M.J., Merelo, J.J. (2009). Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_48
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DOI: https://doi.org/10.1007/978-3-642-02478-8_48
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