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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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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