Use of genetic algorithms with backpropagation in training of feedforward neural networks

Genetic algorithms are searching strategies available for finding the globally optimal solution. The problem of genetic algorithms is that they are inherently slow. A hybrid of genetic and backpropagation algorithms (GA-BP) that should always find the correct global minima without getting stuck at local minima is presented. Various versions of the GA-BP method are presented and experimental results show that GA-BP algorithms are as fast as the backpropagation algorithm and do not get stuck at local minima. The proposed GA-BP algorithms are also not sensitive to the values of momentum and learning rate used in backpropagation and can be made independent of the learning rate and momentum. It is shown that the adaptive GA-BP algorithm can provide the optimal learning rate and better performance than simple backpropagation.<
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