How do you determine which is the best next point where to evaluate the model?

Simulated annealing is blind, what you do with simulated annealing you just move locally around the current point.

If you had a partial picture of the landscape of the function, how would you locate the most interesting unvisited point in the landscape?

I do have to admit that what I'm seeing here is only the application of Bayesian sampling algorithms, there are startups doing similar things for A/B testing and similar.

Original paper was linked from article and is here [1].

Although I'm vaguely familiar with MCMC and Metropolis, it's not nearly enough to pass comment on this advance.

[1] http://arxiv.org/pdf/1402.1694v4.pdf

My understanding of simulated annealing is that the "fudge factor" is chosen and reduced more or less "naively". By which I mean it's initial value and reduction across iterations is hard wired into the execution path before it runs and...the parameters for iteration \i\ are determined before iteration \1\.

My understanding of the article is that the new algorithm determines the parameters of iteration \i\ statistically from the results of iterations \1\ through \i-1\.

From a cursory reading of the article it sounds like they optimize for the best annealling schedule which of course is what every manufacturer of steel has been doing with varying degrees of success for at least a thousand years.