For the number of top configurations k used to initialize each following stage, we know the larger k is, the better results in the next stage since Bayesian Optimization relies on good initial knowledge to fit good regression models (Feurer et al., 2015).
larger k value also leads to high computation cost at the next stage, since these initial settings will have to be evaluated first.