> They only got a bit more clever when connecting different sims running on different conditions. Part of the reason why a lot of these nets are trained with a

> They only got a bit more clever when connecting different sims running on different conditions. Part of the reason why a lot of these nets are trained with added noise, as well as drop-out (randomly disabling 50% of the hidden neurons, every training step). Especially the drop-out tactic is particularly effective at preventing "exploits" of the neural net type, which otherwise appear in the form of large correlated weights (really big weights depending on other really big opposite weights to cancel out--it works, but it doesn't help learning). Either way, adding noisy hurdles helps because exploits are usually edge cases, and noise makes them less dependable, as the region of fitness space very close to an exploitable spot, is usually not very high-ranking at all (which is why you don't want your classifiers ending up there).