Enhanced Radial Basis Function Neural Network Design Using Parallel Evolutionary Algorithms

In this work SymbPar, a parallel co-evolutionary algorithm for automatically design the Radial Basis Function Networks, is proposed. It tries to solve the problem of huge execution time of Symbiotic_CHC_RBF, in which method are based. Thus, the main goal of SymbPar is to automatically design RBF neural networks reducing the computation cost and keeping good results with respect to the percentage of classification and net size. This new algorithm parallelizes the evaluation of the individuals using independent agents for every individual who should be evaluated, allowing to approach in a future bigger size problems reducing significantly the necessary time to obtain the results. SymbPar yields good results regarding the percentage of correct classified patterns and the size of nets, reducing drastically the execution time.

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