Abstract
In this paper, we propose a parallel backpropagation algorithm using the genetic algorithm to reduce the learning time to reach the optimal solution. Genetic algorithm is used in parallel previous to the parallel backpropagation regarding the set of weights in the feedforward neural network as chromosomes, and well-evolved chromosomes (sets of excellent initial weights) are used in the parallel backpropagation. Performance of the proposed algorithm was evaluated experimentally in 5-bit and 8-bit parity problems using the massively parallel computer CP-PACS. The learning speeds were about 3 times and about 7 times faster than those of the simple parallel backpropagation in the 5-bit and the 8-bit parity problems, respectively.
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