Genetic algorithm (GA) - based method of training a neural network is proposed as an alternative to the usual "back-propagation" (BP) algorithm. Considerable changes are needed in the usual design of the GA due to the intrinsic need to provide the algorithm with specified discrete values of the variables (weights). In order to give the GA freedom to select weights from a more-or-less unlimited range of values, the decoding of the binary strings is accompanied by a progressive "shift" of the centre of the range of positive/negative values provided for selection by the algorithm.

The proposed method is applied to a simple beam bending moment situation and to an approximate analysis of a structural grillage. The results obtain by using the GA with shift, are compared with those from a Conventional BP training and with the "exact" results.

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