AutoML 2.0: Should Seed be a tunable param?

One of the tunable parameters is FeatureFraction. Per my understanding this drops randomly the specified portion of columns for each tree. Further, I understand that random seed is used to decide which columns to drop.

In small forests with few trees the specific feature columns that are dropped may have bigger impact than how many features are dropped. One of the columns may lead to overfit and dropping it may improve the results on validation and test sets. However, it is crucial that the correct columns is dropped.

If my understanding is correct, then I believe seed should be one of the tunable parameters. This should be the case at least for algorithms where FeatureFraction is present (FastTree and LightGBM at least). At the moment, it is not present in e.g. FastTreeOption class.

Agree/disagree?