Tuning Fuzzy Perceptron using Parallelized Evolutionary Algorithms
AbstractEvolutionary computation can be used as independent instrument to establish the neural network weights. We assume that the network architecture is known. Some evolutionary algorithms as training procedures of fuzzy perceptron have been proposed before. In this paper, we presented a new hybridization between evolutionary algorithms (used as training procedures of fuzzy perceptron) and parallel algorithms. Using a high performance processor cluster with 28 nodes we will try to get better results in much smaller intervals of time. The kernels used to solve the problem are of the same type, they are eight on each node and each of them is working on the same frequency. The computational results show the validity of new approach in terms runtime, accuracy and flexibility.
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