This paper addresses the problem of tuning the performance of a metaheuristic search algorithm with respect to its parameters. The operational behavior of typical metaheuristic search algorithms is determined by a set of control parameters, which have to be fine-tuned in order to obtain a best performance for a given problem. The principle challenge here is how to provide meaningful settings for an algorithm, obtained as result of better insight in its behavior. In this context, we discuss the idea of learning a model of an algorithm behavior by data mining analysis of parameter tuning results. The study was conducted using the Differential Ant-Stigmergy Algorithm as an example metaheuristic search algorithm.