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dc.contributor | Universitat Politècnica de Catalunya. Departament de Matemàtiques |
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dc.contributor | Universitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica |
dc.contributor.author | Calvet Liñán, Laura |
dc.contributor.author | Juan Pérez, Ángel Alejandro |
dc.contributor.author | Serrat Piè, Carles |
dc.contributor.author | Ries, Jana |
dc.date | 2016-06 |
dc.identifier.citation | Calvet , L., Juan, Á., Serrat, C., Ries, J. A statistical learning based approach for parameter fine-tuning of metaheuristics. "SORT : statistics and operations research transactions", Juny 2016, vol. 40, núm. 1, p. 201-224. |
dc.identifier.citation | 2013-8830 |
dc.identifier.citation | 10.2436/20.8080.02.41 |
dc.identifier.uri | http://hdl.handle.net/2117/102398 |
dc.description.abstract | Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem. |
dc.description.abstract | Peer Reviewed |
dc.language.iso | eng |
dc.rights | info:eu-repo/semantics/openAccess |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject | Mathematical statistics |
dc.subject | Parameter fine-tuning |
dc.subject | metaheuristics |
dc.subject | statistical learning |
dc.subject | biased randomization. |
dc.subject | Estadística matemàtica |
dc.title | A statistical learning based approach for parameter fine-tuning of metaheuristics |
dc.type | info:eu-repo/semantics/publishedVersion |
dc.type | info:eu-repo/semantics/article |