Is creating an ensemble out of the TPOT population useful?

One of the common arguments against population-based optimization methods is that they are significantly slower than methods that work with one (or a few) solutions at a time. I think one smart way to turn that argument on its head would be to see if creating an ensemble out of the TPOT population would be useful.

An initial exploration could be to run TPOT as normal, and collect additional statistics about the performance of the population as an ensemble. This could be done with a very "hacky" version of TPOT; no need to engineer it before we prove this idea's efficacy.

Basically, for every generation:

1) Store the classifications of every individual

2) Use various ensemble methods to combine their classifications into a single classification (min, max, threshold, majority, weighted based on performance on training set)

3) Plot the effectiveness of all of these population ensemble methods over time

What to look for:
- Does the population ensemble perform better than the absolute best individual (early on, later on, always)?
- Does the population ensemble perform better as more generations pass?
- What ensemble method(s) perform best?