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An empirical study of hyperheuristics for managing very large sets of low level heuristics / S. Remde in Journal of the operational research society (JORS), Vol. 63 N° 3 (Mars 2012) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 63 N° 3 (Mars 2012) . - pp. 392–405 Titre : An empirical study of hyperheuristics for managing very large sets of low level heuristics Type de document : texte imprimé Auteurs : S. Remde, Auteur ; P. Cowling, Auteur ; K. Dahal, Auteur Année de publication : 2012 Article en page(s) : pp. 392–405 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Computational  analysis  Heuristics  Hyperheuristics  Machine  learning  Optimisation  Scheduling  Tabu  search Index. décimale : 001.424 Résumé : Hyperheuristics give us the appealing possibility of abstracting the solution method from the problem, since our hyperheuristic, at each decision point, chooses between different low level heuristics rather than different solutions as is usually the case for metaheuristics. By assembling low level heuristics from parameterised components we may create hundreds or thousands of low level heuristics, and there is increasing evidence that this is effective in dealing with every eventuality that may arise when solving different combinatorial optimisation problem instances since at each iteration the solution landscape is amenable to at least one of the low level heuristics. However, the large number of low level heuristics means that the hyperheuristic has to intelligently select the correct low level heuristic to use, to make best use of available CPU time. This paper empirically investigates several hyperheuristics designed for large collections of low level heuristics and adapts other hyperheuristics from the literature to cope with these large sets of low level heuristics on a difficult real-world workforce scheduling problem. In the process we empirically investigate a wide range of approaches for setting tabu tenure in hyperheuristic methods, for a complex real-world problem. The results show that the hyperheuristic methods described provide a good way to trade off CPU time and solution quality. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n3/abs/jors201148a.html [article] An empirical study of hyperheuristics for managing very large sets of low level heuristics [texte imprimé] / S. Remde, Auteur ; P. Cowling, Auteur ; K. Dahal, Auteur . - 2012 . - pp. 392–405. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 63 N° 3 (Mars 2012) . - pp. 392–405 Mots-clés : Computational  analysis  Heuristics  Hyperheuristics  Machine  learning  Optimisation  Scheduling  Tabu  search Index. décimale : 001.424 Résumé : Hyperheuristics give us the appealing possibility of abstracting the solution method from the problem, since our hyperheuristic, at each decision point, chooses between different low level heuristics rather than different solutions as is usually the case for metaheuristics. By assembling low level heuristics from parameterised components we may create hundreds or thousands of low level heuristics, and there is increasing evidence that this is effective in dealing with every eventuality that may arise when solving different combinatorial optimisation problem instances since at each iteration the solution landscape is amenable to at least one of the low level heuristics. However, the large number of low level heuristics means that the hyperheuristic has to intelligently select the correct low level heuristic to use, to make best use of available CPU time. This paper empirically investigates several hyperheuristics designed for large collections of low level heuristics and adapts other hyperheuristics from the literature to cope with these large sets of low level heuristics on a difficult real-world workforce scheduling problem. In the process we empirically investigate a wide range of approaches for setting tabu tenure in hyperheuristic methods, for a complex real-world problem. The results show that the hyperheuristic methods described provide a good way to trade off CPU time and solution quality. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n3/abs/jors201148a.html