The success of an artificial neural network depends
much on the training phase. Techniques for training neural
network based on gradient are partially satisfying and are widely
used in practice. However, in several cases which has special
error surface similar to a deep cleft, these algorithms seem to
work slowly and encounter local extreme values. Authors of this
paper propose the use of genetic algorithm in combination with
the “cleft-overstep” algorithm to improve the training process of
neural network which has special error surface and illustrate this
usage through a simple application in text recognition. First, An
MLP artificial neural network with cleft-similar error surface is
trained using back propagation algorithm and the results are
analyzed. Next, the paper describes the usage of the proposed
method to improve the training process of neural network on two
aspects: correctness and rate of convergence. Implementation is
done in and results obtained from Matlab environment.

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