{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T10:06:35Z","timestamp":1725876395493},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319514680"},{"type":"electronic","value":"9783319514697"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-51469-7_28","type":"book-chapter","created":{"date-parts":[[2016,12,24]],"date-time":"2016-12-24T21:23:01Z","timestamp":1482614581000},"page":"330-340","source":"Crossref","is-referenced-by-count":0,"title":["Combining Genetic Algorithm with the Multilevel Paradigm for the Maximum Constraint Satisfaction Problem"],"prefix":"10.1007","author":[{"given":"Noureddine","family":"Bouhmala","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,12,25]]},"reference":[{"key":"28_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-540-30498-2_15","volume-title":"Advances in Artificial Intelligence \u2013 IBERAMIA 2004","author":"D\u00c1 Huerta-Amante","year":"2004","unstructured":"Huerta-Amante, D.\u00c1., Terashima-Mar\u00edn, H.: Adaptive penalty weights when solving congress timetabling. In: Lema\u00eetre, C., Reyes, C.A., Gonz\u00e1lez, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 144\u2013153. Springer, Heidelberg (2004). doi: 10.1007\/978-3-540-30498-2_15"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1155\/2015\/637809"},{"key":"28_CR3","unstructured":"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)"},{"key":"28_CR4","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/0004-3702(89)90037-4","volume":"38","author":"R Dechter","year":"1989","unstructured":"Dechter, R., Pearl, J.: Tree clustering for constraint networks. Artif. Intell. 38, 353\u2013366 (1989)","journal-title":"Artif. Intell."},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-319-08016-1_7","volume-title":"Frontiers in Algorithmics","author":"Z Fang","year":"2014","unstructured":"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\u201381. Springer, Heidelberg (2014). doi: 10.1007\/978-3-319-08016-1_7"},{"key":"28_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/BFb0017440","volume-title":"Principles and Practice of Constraint Programming-CP97","author":"P Galinier","year":"1997","unstructured":"Galinier, P., Hao, J.-K.: Tabu search for maximal constraint satisfaction problems. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 196\u2013208. Springer, Heidelberg (1997). doi: 10.1007\/BFb0017440"},{"key":"28_CR7","unstructured":"Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: Proceedings of the AAAI 1996, pp. 246\u2013252 (1996)"},{"key":"28_CR8","volume-title":"Adaptation in Natural and Artificial Systems","author":"J Holland","year":"1975","unstructured":"Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)"},{"key":"28_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/3-540-46135-3_16","volume-title":"Principles and Practice of Constraint Programming - CP 2002","author":"F Hutter","year":"2002","unstructured":"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\u2013248. Springer, Heidelberg (2002). doi: 10.1007\/3-540-46135-3_16"},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-642-01818-3_30","volume-title":"Advances in Artificial Intelligence","author":"H-J Lee","year":"2009","unstructured":"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\u2013232. Springer, Heidelberg (2009). doi: 10.1007\/978-3-642-01818-3_30"},{"key":"28_CR11","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0004-3702(92)90007-K","volume":"58","author":"S Minton","year":"1992","unstructured":"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\u2013205 (1992)","journal-title":"Artif. Intell."},{"key":"28_CR12","unstructured":"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\u201345 (1993)"},{"key":"28_CR13","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10732-010-9131-5","volume":"17","author":"W Pullan","year":"2011","unstructured":"Pullan, W., Mascia, F., Brunato, M.: Cooperating local search for the maximum clique problems. J. Heuristics 17, 181\u2013199 (2011)","journal-title":"J. Heuristics"},{"key":"28_CR14","unstructured":"Schuurmans, D., Southey, F., Holte, E.: The exponentiated subgradient algorithm for heuristic Boolean programming. In: 17th International Joint Conference on Artificial Intelligence, pp. 334\u2013341. Morgan Kaufmann Publishers, San Francisco (2001)"},{"issue":"1","key":"28_CR15","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1023\/A:1008287028851","volume":"12","author":"E Shang","year":"1998","unstructured":"Shang, E., Wah, B.: A discrete Lagrangian-based global-search method for solving satisfiability problems. J. Glob. Optim. 12(1), 61\u201399 (1998)","journal-title":"J. Glob. Optim."},{"key":"28_CR16","series-title":"International Series in Operation Research and Management Science","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/0-306-48056-5_7","volume-title":"Handbook of Metaheuristics","author":"C Voudouris","year":"2003","unstructured":"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\u2013218. Springer, Heidelberg (2003)"},{"key":"28_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/3-540-61479-6_23","volume-title":"Over-Constrained Systems","author":"RJ Wallace","year":"1996","unstructured":"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\u2013216. Springer, Heidelberg (1996). doi: 10.1007\/3-540-61479-6_23"},{"issue":"2","key":"28_CR18","doi-asserted-by":"crossref","first-page":"191","DOI":"10.14257\/ijhit.2014.7.2.18","volume":"7","author":"W Xu","year":"2014","unstructured":"Xu, W.: Satisfiability transition and experiments on a random constraint satisfaction problem model. Int. J. Hybrid Inf. Technol. 7(2), 191\u2013202 (2014)","journal-title":"Int. J. Hybrid Inf. Technol."},{"issue":"1","key":"28_CR19","doi-asserted-by":"crossref","first-page":"379","DOI":"10.12785\/amis\/070147","volume":"7","author":"Y Zhou","year":"2013","unstructured":"Zhou, Y., Zhou, G., Zhang, J.: A hybrid glowworm swarm optimization algorithm for constrained engineering design problems. Appl. Math. Inf. Sci. 7(1), 379\u2013388 (2013)","journal-title":"Appl. Math. Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Big Data"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-51469-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,25]],"date-time":"2017-06-25T06:43:01Z","timestamp":1498372981000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-51469-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319514680","9783319514697"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-51469-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}