[davmre]

The point of Bayesian optimization is that, once you've evaluated your first parameter setting (or round of settings, if you're running in parallel), you no longer have a uniform prior; you have a posterior: that evaluation gave you new information, and you should use that to be smart about where you evaluate next instead of just continuing with a fixed grid.

There's also an argument (http://jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf) that random search is better than grid search, because when only a few of the parameters really matter (but you don't know which ones), grid search wastes effort on scanning the unimportant parameters with the important parameters held fixed, but each point in a random search evaluates a new setting of the important parameters.

All this said, certainly grid search is way better than not optimizing parameters at all. My guess is that was the spirit in which this suggestion was made, so I wouldn't take it as a reason to discount the guy.