Abstract
Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This paper introduces a hybrid approach combining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promising performances achieved by the proposed approach are demonstrated by comparisons made to solve conventional random benchmark problems.
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References
Huerta-Amante, D.Á., Terashima-Marín, H.: Adaptive penalty weights when solving congress timetabling. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 144–153. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30498-2_15
Bouhmala, N.: A variable depth search algorithm for binary constraint satisfaction problems. Math. Probl. Eng. 2015, 10 (2015). Article ID 637809, doi:10.1155/2015/637809
Davenport, A., Tsang, E., Wang, C., Zhu, K.: Genet: a connectionist architecture for solving constraint satisfaction problems by iterative improvement. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (1994)
Dechter, R., Pearl, J.: Tree clustering for constraint networks. Artif. Intell. 38, 353–366 (1989)
Fang, Z., Chu, Y., Qiao, K., Feng, X., Xu, K.: Combining edge weight and vertex weight for minimum vertex cover problem. In: Chen, J., Hopcroft, J.E., Wang, J. (eds.) FAW 2014. LNCS, vol. 8497, pp. 71–81. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08016-1_7
Galinier, P., Hao, J.-K.: Tabu search for maximal constraint satisfaction problems. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 196–208. Springer, Heidelberg (1997). doi:10.1007/BFb0017440
Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: Proceedings of the AAAI 1996, pp. 246–252 (1996)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Hutter, F., Tompkins, D.A.D., Hoos, H.H.: Scaling and probabilistic smoothing: efficient dynamic local search for SAT. In: Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 233–248. Springer, Heidelberg (2002). doi:10.1007/3-540-46135-3_16
Lee, H.-J., Cha, S.-J., Yu, Y.-H., Jo, G.-S.: Large neighborhood search using constraint satisfaction techniques in vehicle routing problem. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS (LNAI), vol. 5549, pp. 229–232. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01818-3_30
Minton, S., Johnson, M., Philips, A., Laird, P.: Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling scheduling problems. Artif. Intell. 58, 161–205 (1992)
Morris, P.: The breakout method for escaping from local minima. In: Proceeding AAAI 1993, Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 40–45 (1993)
Pullan, W., Mascia, F., Brunato, M.: Cooperating local search for the maximum clique problems. J. Heuristics 17, 181–199 (2011)
Schuurmans, D., Southey, F., Holte, E.: The exponentiated subgradient algorithm for heuristic Boolean programming. In: 17th International Joint Conference on Artificial Intelligence, pp. 334–341. Morgan Kaufmann Publishers, San Francisco (2001)
Shang, E., Wah, B.: A discrete Lagrangian-based global-search method for solving satisfiability problems. J. Glob. Optim. 12(1), 61–99 (1998)
Voudouris, C., Tsang, E.: Guided local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operation Research and Management Science, vol. 57, pp. 185–218. Springer, Heidelberg (2003)
Wallace, R.J., Freuder, E.C.: Heuristic methods for over-constrained constraint satisfaction problems. In: Jampel, M., Freuder, E., Maher, M. (eds.) OCS 1995. LNCS, vol. 1106, pp. 207–216. Springer, Heidelberg (1996). doi:10.1007/3-540-61479-6_23
Xu, W.: Satisfiability transition and experiments on a random constraint satisfaction problem model. Int. J. Hybrid Inf. Technol. 7(2), 191–202 (2014)
Zhou, Y., Zhou, G., Zhang, J.: A hybrid glowworm swarm optimization algorithm for constrained engineering design problems. Appl. Math. Inf. Sci. 7(1), 379–388 (2013)
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Bouhmala, N. (2016). Combining Genetic Algorithm with the Multilevel Paradigm for the Maximum Constraint Satisfaction Problem. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_28
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