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Using genetic algorithms to improve the search of the weight space in cascade-correlation neural network

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4 Author(s)
Mayer, E. A. ; University of Toledo, OH 43606, U.S.A. ; Cios, K. J. ; Berke, L. ; Vary, A.

In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead of modifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.

Published in:

Systems Engineering and Electronics, Journal of  (Volume:6 ,  Issue: 2 )