Hi thanks for the playbook! I see some articles showing how Hyperband or ASHA can be used to boost the speed of hyperparameter searching. Shortly speaking, it is:

On a high level, ASHA terminates trials that are less promising and allocates more time and resources to more promising trials. As our optimization process becomes more efficient, we can afford to increase the search space by 5x, by adjusting the parameter num_samples. src

Thus, I wonder whether it is a good idea to utilize this scheduler (in addition to quasi-random search or bayesian)? IMHO they are somehow orthogonal, and we can use both ASHA and quasi-random search. Then, quasi-random search will propose (quasi) random hyper-parameters while ASHA will throw away some of the bad ones.

I am willing to contribute (e.g. making a PR)!