You are implementing hyperparameter tuning using Bayesian sampling for model training in an Azure Machine Learning notebook. The workspace uses a compute cluster with 20 nodes. The code uses a Bandit termination policy with a slack factor of 0.2 and a HyperDriveConfig instance with max_concurrent_runs set to 10.

To increase the effectiveness of the tuning process by improving sampling convergence, which sampling convergence method should you select?