It is quite frequent that optimization algorithms have quite some parameters controlling their behavior (cooling schedule of a Simulated Annealing, length of the tabu list for a Tabu Search, population size for a Genetic Algorithm, etc...).
One will typically spend quite some time fine tuning those parameters using a "representative" set of instances. However it can happen that a set of parameters will work very well on one instance but quite poorly on another one (particularly if the set of representative instances is quite heterogeneous).
It would seem interesting, based on some instance characteristics to determine (probably using supervised learning techniques) before solving which parameters values are the most likely to give good results.
I have tried to find some information about this idea in the literature but could not find anything. Do you know of any success/failure stories related to this idea?
On a related note I know about Adaptive Large Neighborhood Search (ALNS), where one tries to figure out during the search which neighborhoods are the most efficient for the instance being currently solved. I am more interested about an approach where the parameters are completely determined before starting the actual solving process.