Abstract
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods for the optimization of modular neural networks. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
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References
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, Anchorage, Alaska, pp. 84–89. IEEE, Los Alamitos (1998)
Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)
Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)
Castillo, O., Huesca, G., Valdez, F.: In: Proceedings of the International Conference on Artificial Intelligence, IC-AI 2004, June 21-24, vol. 1, pp. 98–104 (2004)
Castillo, O., Valdez, F., Melin, P.: Hierarchical Genetic Algorithms for topology optimization in fuzzy control systems. International Journal of General Systems 36(5), 575–591 (2007)
Castillo, O., Melin, P.: Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory. IEEE Transactions on Neural Networks 13(6), 1395–1408 (2002)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995); Lu, J.-G.: Title of paper with only the first word capitalized. J. Name Stand. Abbrev. (in press)
Emmeche, C.: Garden in the Machine. In: The Emerging Science of Artificial Life, p. 114. Princeton University Press, Princeton (1994)
Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE transactions on neural networks 5(1), 3–14 (1994)
Goldberg, D.: Genetic Algorithms. Addison-Wesley, Reading (1988)
Holland, J.H.: Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor (1975)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceeding of IEEE conference on Evolutionary Computation, pp. 1671–1676 (2002)
Kennedy, J., Mendes, R.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer, Heidelberg (1999)
Montiel, O., Castillo, O., Melin, P., Rodriguez, A., Sepulveda, R.: Human evolutionary model: A new approach to optimization. Inf. Sci. 177(10), 2075–2098 (2007)
Valdez, F., Melin, P., Castillo, O.: Evolutionary Computing for the Optimization of Mathematical functions. In: Analysis and Design of Intelligent Systems Using Soft Computing Techniques. Advances in Soft Computing, vol. 41 (June 2007)
Valdez, F., Melin, P.: Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization. In: NAFIPS, San Diego CA USA, June 2007, pp. 598–602 (2007)
Veeramachaneni, K., Osadciw, L., Yan, W.: Improving Classifier Fusion Using Particle Swarm Optimization. In: IEEE Fusion Conference, Italy (July 2006)
Wei, W., Jiatao, S., Zhongzxiu, Y., Zheru., C.: Wavelet-based Illumination Compensation for Face Recognition using Eifenface Method Intelligent Control and Automation. In: WCICA 2006, 21-23 June 2006, vol. 2, pp. 10356–10360 (2006)
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Valdez, F., Melin, P., Licea, G. (2009). Modular Neural Networks Architecture Optimization with a New Evolutionary Method Using a Fuzzy Combination Particle Swarm Optimization and Genetic Algorithms. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Studies in Computational Intelligence, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04516-5_13
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DOI: https://doi.org/10.1007/978-3-642-04516-5_13
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