Random element in fitness function genetic algorithm
Normally you use a seed for genetic algorithms, which should be fixed. It will always generate the same "random" childs sequentially, which makes your approach reproducible. So the genetic algorithm is kind of pseudo-random. That is state of art how to perform genetic algorithms.
So I am using a genetic algorithm to train a feedforward neural network, tasked with recognizing a function given to the genetic algorithm. I.e x = x**2 or something more complicated obviously.
I realized I am using random inputs in my fitness function, which causes the fitness to be somewhat random for a member of the population, however, still in line with how close it is to the given function obviously. A colleague remarked that it is stranged that the same member of the population doesnt always get the same fitness, which I agree is a little unconventional. However, it got me thinking, is there any reason why this would be bad for the genetic algorithm? I actually think it might be quite good because it enables me to have a rather small testset, speeding up number of generations while still avoiding overfitting to any given testest.
Does anyone have experience with this?
(fitness function is MSE compared to given function, for a randomly generated testset of 10 iterations)