The genetic optimizers I am familiar with work on parameters of a given strategy. My platform tests different feature combinations that can make up a strategy. Of course there are deep learning routines that may use some sort of genetic optimization and then spit out strategy code but my approach uses highly supervised learning that is configured by the user.I haven't gone through the whole design but maybe you can discuss the differences between this and a giant genetic optimizer.
The genetic algo is even more unconstrained, it will build expression trees of time series transformations, combine them until you get a "fit"
