Abstract
A self-optimizing approach for complex classifications is proposed in this paper to construct dynamical radial basis function neural network (RBFNN) models based on a specially designed genetic algorithm (GA). The algorithm adopts a matrix-form mixed encoding and specifically designed genetic operators to optimize the decayed-radius selected clustering (DRSC) process by co-evolving all of the parameters of the network’s layout. The individual fitness is evaluated as a multi-objective optimization task and the weights between the hidden layer and the output layer are calculated by the pseudo-inverse algorithm. Experimental results on eight UCI datasets show that the GA-RBFNN can produce a higher accuracy of classification with a much simpler network structure and outperform those models of neural network based on other training methods.
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Tian, J., Li, M., Chen, F. (2007). An Evolutionary RBFNN Learning Algorithm for Complex Classzification Problems. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_54
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DOI: https://doi.org/10.1007/978-3-540-72393-6_54
Publisher Name: Springer, Berlin, Heidelberg
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