Optimisation methods would be very useful.

This is a general stochastic method for finding the global optimum of an unknown function in any number of dimensions. It is most useful for expensive black box functions (i.e. a objective 'function' which takes minutes/hours for a single iteration).

This method has various added benefits. One being: As the optimisation is performed, a response surface is fitted to the unknown function which can be used as a surrogate for future function evaluations. The 'kriging' response model parameterises the unknown functions input variables in terms of output sensitivity and smoothness.

An outline of the original method is described in:

Jones, D., Schonlau, M., and Welch, W., 1998, “Efficient Global Optimization
of Expensive Black-Box Functions,” J. Global Optim., 134, pp. 455–492.

A copy of which can be found via Google search here:

http://www.ressources-actuarielles.net/EXT/ISFA/1226.nsf/9c8e3fd4d8874d60c1257052003eced6/f84f7ac703bf5862c12576d8002f5259/$FILE/Jones98.pdf