When conducting a grid search over a range of parameters of a predictive model which is itself subject to randomness (such as a random forest with bagged features), should you set a seed for the predictive model, so that for each round of the grid search, the model is initialized the same? It seems straightforward, that you ONLY want to test the parameters, and the less variance, the better, but is there any scientific consensus on this?