  This paper presents a hybrid approach called frequent pattern based search
that combines data mining and optimization. The proposed method uses a data
mining procedure to mine frequent patterns from a set of high-quality solutions
collected from previous search, and the mined frequent patterns are then
employed to build starting solutions that are improved by an optimization
procedure. After presenting the general approach and its composing ingredients,
we illustrate its application to solve the well-known and challenging quadratic
assignment problem. Computational results on the 21 hardest benchmark instances
show that the proposed approach competes favorably with state-of-the-art
algorithms both in terms of solution quality and computing time.
