The problem of genetic algorithms is that they are inherently slow. In this paper we present a hybrid of genetic and back-propagation algorithms (GA-BP) which should always find the correct global minima without getting stuck at local minima. Various versions of the GA-BP method are presented and experimental results show that GA-BP algorithms are as fast as the back-propagation 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 back-propagation 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 back-propagation.