Feature selection using TPOT - implement a subset selector?

The previous ["feature selection" question](https://github.com/EpistasisLab/tpot/issues/710) reminded me to ask this slightly different question.

I was wondering roughly how one might go about implementing a "`SubsetSelector`" pipeline element to go in the very first stage of the pipeline. The job of this element would be to pass only certain subsets of the features onwards. If someone would "point the way", I would gladly try to implement this.

As an example, lets say our six features are `[f1, f2, f3, g1, g2, g3]`. I would define two families `[f1, f2, f3]` and `[g1, g2, g3]`. The possible hyper-parameters ("DNA") of this `SubsetSelector` would be `[1,0]` for only the `f` features, `[0,1]` for only the `g` features, and `[1,1]` for both features. The hyper-parameter of this `SubsetSelector` element seems to be well-suited for genetic optimization.

This pipeline element is not (?) offered by sklearn (maybe because it is trivial and useless in that context), but I think it makes a lot of sense in the context of tpot.

Thoughts? Suggestions? I'm new to genetic programming, tpot, and pipeline optimization, so your thoughts would be greatly appreciated.