Well it turns out that there’s actually a ton of literature out there regarding automated CNN and RNN architecture searches (thanks for the DARTS link and corresponding rabbit hole @ptrblck ).
I’m only 4 articles deep at this point, but none of the approaches thus far utilize truly randomized layer structures/parameterizations for a seed.  They all seem to require the user to supply a predetermined set of potential architectures and then build networks via ensembles of those possible networks (i.e. to scale a simple CIFAR net to a multi use case like ImageNet through repetition of a proven “cell” like so) or to examine multiple connectivity schemas for a predefined set of cells as DARTS does.
With that in mind, do you think that randomized approaches were examined and discarded due performance issues or computational issues?  If the latter, do you think the amount of progress in the last couple of years might have loosened computational constraints sufficiently to allow an approach like this to actually work?
Thanks for any feedback!
–SEH