It's also using a simple neural network, which is the target of the genetic algorithm, if I understood it right. I haven't seen this combination often - does that make sense in general, or is this just interesting as in playing around with those concepts?
it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking).
in some cases you might lack an easy way to calculate a fitness score out of the NN performance, which is needed to run the GA.
i tried training a simple NN with a stupid hill climber some time ago but quickly hit a roadblock even with very few neurons because of local minima ... or maybe bugs.
i guess for more complicated problems the pure GA training method might just not be "cost effective" (i.e. time/quality tradeoff).
> it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking).
Different applications, though. Backtracking in the normal sense needs input and expected output (e.g. lots of training data), while GA/EA learns to solve it without explicit wrong/correct actions, just the score at the end.
