The objectives of this paper are twofold; we first demonstrate the flexibility of the mesh adaptive direct search (MADS) in identifying locally optimal algorithmic parameters. This is done by devising a general framework for parameter tuning. The framework makes provision for surrogate objectives. Parameters are sought so as to minimize some measure of performance of the algorithm being fine-tuned. This measure is treated as a black-box and may be chosen by the user. Examples are given in the text.

The second objective illustrates this framework by specializing it to the identification of locally optimal trust-region parameters in unconstrained optimization. Parameters are identified that minimize, in a certain sense, the computational time required to solve a set of problems from the CUTEr collection. Each function call may take several hours and may not always return a predictable result. A surrogate function, taylored to the experiment at hand, is used to guide the MADS towards a local solution.

The parameters thus identified differ from traditionally used values, and are used to solve a problem from the CUTEr collection that remained otherwised unsolved in a reasonable time using traditional values.