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- W1513947218 abstract "A new hybrid method for feed forward neural network training, which combines differential evolution algorithm with a gradient-based approach is proposed. In the method, after each generation of differential evolution, a number of iterations of the conjugate gradient optimization algorithm is applied to each new solution created by the mutation and crossover operators. The experimental results show, that in comparison to the standard differential evolution the hybrid algorithm converges faster. Although this convergence is slower than that of classical gradient based methods, the hybrid algorithm has significantly better capability of avoiding local optima." @default.
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- W1513947218 date "2009-01-01" @default.
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- W1513947218 title "Training neural networks with a hybrid differential evolution algorithm" @default.
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