Tuning Parameters for Boosting/Bagging/Random Forest

Hello

I want to use tree-based classifiers for my classifiaction problem. I'm thinking about bagging, boosting (AdaBoost, LogitBoost, RUSBoost) and Random Forest but I'm unsure about the tuning parameters, i.e. which range I should search.

I'm using the TreeBagger and fitensemble method from Matlab. I'm unsure about the following parameters:

• Number of iterations / Trees
• Sampling with or without replacement? If without replacement what in bag fraction to take?
• Minimum Leaf Size
• Minimum Parent Size
• Maximum number of decision splits
• Learning rate for shrinkage
• RatioToSmallest (Every element of this vector is the sampling proportion for this class with respect to the class with fewest observations). I have highly imbalanced classes.
• MarginPrecision
• (The level of pruning and value of the pruning cost the tree should pruned to (alpha))

I would be very happy if somebody could give a quick help.