Neural networks with multiple training algorithms

I have used a variety of neural network training algorithms; backprop, genetic algorithms, particle swarm methods, etc.  Different algorithms have different advantages and disadvantages.  Would it make sense to switch back and forth between two or more algorithms as training proceeds?  I have often found genetic algorithms to be slower than backprop.  Might one start training with backprop to speed things up and then switch to a genetic algorithm later on to escape from any local minima?