Using TPOT's GP framework to select features

Hi. I wonder if it's possible to use TPOT's genetic programming framework to select the best subset of features. I already have an idea of what classification algorithm and parameters work well and I do not want to optimize the entire ML pipeline. I just would like to create a population of smaller feature sets out of ~1000 features I have and use cross-over/mutation to find the best set while using my static estimator for model evaluation. Can I do that with TPOT? If so, how might I go about doing it? Thanks!