Question

Is a genetic algorithm the most efficient way to optimize the number of hidden nodes and the amount of training done on an artificial neural network?

I am coding neural networks using the NNToolbox in Matlab. I am open to any other suggestions of optimization techniques, but I’m most familiar with GA’s.

Answer 1

Actually, there are multiple things that you can optimize using GA regarding NN.
You can optimize the structure (number of nodes, layers, activation function etc.).
You can also train using GA, that means setting the weights.

Genetic algorithms will never be the most efficient, but they usually used when you have little clue as to what numbers to use.

For training, you can use other algorithms including backpropagation, nelder-mead etc..

You said you wanted to optimize number hidden nodes, for this, genetic algorithm may be sufficient, although far from “optimal”. The space you are searching is probably too small to use genetic algorithms, but they can still work and afaik, they are already implemented in matlab, so no biggie.

What do you mean by optimizing amount of training done? If you mean number of epochs, then that’s fine, just remember that training is somehow dependent on starting weights and they are usually random, so the fitness function used for GA won’t really be a function.