Hi, I am running a NEAT with more than 28 features and comparing it with a deep backprop network. I am running a regression problem.After running multiple configurations(pop size, crossover, mutation rate, connection add rate , etc). I am observing that neat is leaving out important features from the network if initial state is unconnected. If i start with fully connected network , then the accuracy is dropping a lot. Not to mention both the methods are underperforming deep fully connected network. Has anybody else faced this issue or does anybody has a thought on this problem?