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

When data mining meets optimization: A case study on the quadratic assignment problem