Useful paper for VMC optimization?

I ran across this paper, An informational approach to the global optimization of expensive-to-evaluate functions, and wondered if it might be useful for VMC optimization?

The type of problem being solved seems to fit:

1. Function is expensive to evaluate.
   
2. Function evaluation may be noisy.
   

Many optimization methods estimate an optimal point and evaluate the function at that point. The method described in the paper estimates the uncertainty in the knowledge of the function, and evaluates the function where it will best improve our knowledge of the function.

Some potential issues:

• This method doesn't make use of gradient information, which may make it less competitive than methods that do use gradient information.


• How well does it scale with the number of parameters? The authors present a method to keep the cost under control as the dimension increases, but is the method still effective then?