I'm curious, I've thought about using genetic algorithms for training neural nets before, but haven't seen it mentioned much. I imagine PSO could work as well. What is the general concensus on using algorithms other than back propagation for neural net learning? When is it appropriate / not appropriate?
Some people frown upon training neural networks with genetic algorithms, just because gen algs are so random and hard to dissect. I think it's silly to discard anything that is a potential solution to a problem.
I go by a rule of thumb like this:
- If you are able to collect lots of training data (inputs and valid outputs) then use back-propagation. It's faster and you might get better results.
- If you don't know the outputs for inputs or if there are simply too many possible combinations (such as in the case of a game), then use a genetic algorithm. It's effectively a search engine that finds the best solution within the problem space (the solution being the optimal weights for the neural network).
