Abstract
The maximum diversity problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among themselves. The identification of such solution is an NP-hard problem. In this work, we propose a hybrid GRASP metaheuristic for the MDP that incorporates a data mining process. Data mining refers to the extraction of new and potentially useful knowledge from datasets in terms of patterns and rules. We believe that data mining techniques can be used to extract patterns that represent characteristics of sub-optimal solutions of a combinatorial optimization problem. Therefore these patterns can be used to guide the search for better solutions in metaheuristics procedures. Performance comparison between related work and the proposed hybrid heuristics is provided. Experimental results show that the new hybrid GRASP is quite robust and, mainly, this strategy is able to find high-quality solutions in less computational time.
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References
Andrade, P.M.F., Plastino, A., Ochi, L.S., Martins, S.L.: GRASP for the maximum diversity problem. In: Procs. of MIC 2003. CD-ROM Paper: MIC03_15 (2003)
Andrade, M.R.Q., Andrade, P.M.F., Martins, S.L., Plastino, A.: GRASP with path-relinking for the maximum diversity problem. In: Nikoletseas, S.E. (ed.) WEA 2005. LNCS, vol. 3503, pp. 558–569. Springer, Heidelberg (2005)
Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995)
Ghosh, J.B.: Computational aspects of the maximum diversity problem. Operations Research Letters 19, 175–181 (1996)
Glover, F., Hersh, G., McMillan, C.: Selecting subsets of maximum diversity, MS/IS Report No. 77-9, University of Colorado at Boulder (1977)
Glover, F., Kuo, C.-C., Dhir, K.S.: Integer programming and heuristic approaches to the minimum diversity problem. J. of Bus. and Management 4, 93–111 (1996)
Goethals, B., Zaki, M.J.: Advances in frequent itemset mining implementations: Introduction to FIMI 2003. In: IEEE ICDM FIMI Workshop (2003)
Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: IEEE ICDM FIMI Workshop (2003)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishing, San Francisco (2000)
Kochenberger, G., Glover, F.: Diversity data mining, Working Paper. The University of Mississipi (1999)
Orlando, S., Palmerimi, P., Perego, R.: Adaptive and resource-aware mining of frequent sets. In: IEEE Intl. Conf. on Data Mining, pp. 338–345 (2002)
Prais, M., Ribeiro, C.C.: Reactive GRASP: An application to a matrix decomposition problem in TDMA traffic assignment. INFORMS Journal on Computing 12, 164–176 (2000)
Resende, M.G.C., Ribeiro, C.C.: Greedy randomized adaptive search procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249 (2003)
Ribeiro, M.H., Trindade, V., Plastino, A., Martins, S.: Hybridization of GRASP metaheuristic with data mining techniques. In: Workshop on Hybrid Metaheuristics in conjunction with the 16th European Conf. on Artificial Intelligence, pp. 69–78 (2004)
Silva, G.C., Ochi, L.S., Martins, S.L.: Experimental comparison of greedy randomized adaptive search procedures for the maximum diversity problem. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 498–512. Springer, Heidelberg (2004)
Talbi, E.G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8, 541–564 (2002)
Weitz, R., Lakshminarayanan, S.: An empirical comparison of heuristic methods for creating maximally diverse groups. J of the Op. Res. Soc. 49, 635–646 (1998)
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Santos, L.F., Ribeiro, M.H., Plastino, A., Martins, S.L. (2005). A Hybrid GRASP with Data Mining for the Maximum Diversity Problem. In: Blesa, M.J., Blum, C., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. HM 2005. Lecture Notes in Computer Science, vol 3636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546245_11
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DOI: https://doi.org/10.1007/11546245_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28535-9
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