This paper describes the simulation of two hybrid evolutionary algorithms (EAs) to the feedforward neural networks (NNs) used in classification problems. Besides backpropagation algorithm, simple geneticalgorithm and...
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This paper describes the simulation of two hybrid evolutionary algorithms (EAs) to the feedforward neural networks (NNs) used in classification problems. Besides backpropagation algorithm, simple genetic algorithm and random search algorithm, the paper considers simple hybrid geneticalgorithm and hybrid randomsearchalgorithm. The objective is to analyze the performance of hybrid geneticalgorithm and hybrid randomsearchalgorithm over other discussed algorithms for the classification problem. The experiments considered a feedforward NN trained with simple hybrid geneticalgorithm/hybrid randomsearchalgorithm and 39 types of network structures and artificial data sets. In most cases, the hybrid evolutionary feedforward NNs seemed to be better than the other algorithms. We found few differences in the performance of the networks trained by applying the hybrid geneticalgorithms, but found ample differences in the execution time. The results suggest that the hybrid evolutionary feedforward NN might be the best algorithm on the data sets we tested. (c) 2006 Elsevier B.V. All rights reserved.
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