Data Mining-Assisted Parameter Tuning of a Search Algorithm

Jurij Šilc, Katerina Taškova, Peter Korošec


The main purpose of this paper is to show how using data-mining technique to tackle the problem of tuning the performance of a meta-heuristic search algorithm with respect to its parameters. The operational behavior of typical meta-heuristic 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 meta-heuristic search algorithm.

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