Tabu Search (TS) is a well known local search method which has been widely used for solving AI problems. Different versions of TS have been proposed in the literature, and many features of TS have been considered and tested experimentally. The feature that is present in almost all TS variants is the so called (short-term) tabu list which is recognised as the crucial issue of TS. However, the definition of the parameters associated with the tabu list remains in most TS applications still a handcrafted activity.

In this work we undertake a systematic study of the relative influence of few relevant tabu list features on the performances of TS solvers. In particular, we apply statistical methods for the design and analysis of experiments. The study focuses on a fundamental theoretical problem (GRAPH COLOURING) and on one of its practical specialisation (EXAMINATION TIMETABLING), which involves specific constraints and objectives. The goal is to determine which TS features are more critical for the good performance of TS in a general context of applicability.

The general result is that, when the quantitative parameters are well tuned, the differences with respect to qualitative parameters become less evident.