If I compare back-propagation to feed-forward neuro-modulation, the latter is unsupervised (requires no labeled data set).But putting it into a genetic algorithm to refine topology and weights, the GA will require fitness function, which means you need a labeled data set to compare with.Would that renders FF neuro-modulation a supervised learning in that case?Is there any way to get Unsupervised learning (I have no labeled datasets) using NeuroEvolution?
Neuroevolution is it not Supervised Learning?
neural networks;genetic algorithms;unsupervised learning
the GA will require fitness function, which means you need a labeled data set to compare with.That conclusion is wrong. Yes, sometimes your fitenss function will use labeled data. For example, if you want to train an XOR gate or any other known function. However, there is no advantage of training a function with neuroevolution versus backpropagation - except for the fact that you might discover some new architectures which solve the solution very well.You don't always need a labelled dataset for neuroevolutionTake for example IAMDinosaur, which trains neural networks through a genetic algorithm - however, the optimal solution is not known. There is no labelling of input data, all it does is calculate the fitness from the score.