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dc.contributorSerrat Piè, Carles
dc.contributorJuan Pérez, Angel Alejandro
dc.contributor.authorCalvet Liñán, Laura
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtica Aplicada I
dc.date.accessioned2014-10-20T11:06:36Z
dc.date.available2014-10-20T11:06:36Z
dc.date.issued2014-06
dc.identifier.urihttp://hdl.handle.net/2099.1/23143
dc.description.abstractMetaheuristics are an approximate method widely used to solve many hard optimization problems in a multitude of fields. They depend on a variable number of parameters. Despite the fact that they are usually capable of finding good solutions within a reasonable time, the difficulty in selecting appropriate values for their parameters causes a loss of efficiency, as it normally requires much time, skills and experience. This master degree s thesis provides a survey of the main approaches developed in the last decade to tackle the problem of choosing a good set of parameter values, called the Parameter Setting Problem, and compares them from a methodological point of view focusing on the statistical procedures used so far by the scientific community. This analysis is accompanied by a proposal of a general methodology. The results of applying it to fine-tuning the parameters of a hybrid algorithm, which combines Biased Randomization with the Iterated Local Search metaheuristic, for solving the Multi-depot Vehicle Routing Problem are also reported. The computational experiment shows promising results and the need / suitability of further investigations based on a wider range of statistical learning techniques. Along these same lines, different suggestions for future work are described. In addition, this work highlights the importance of statistics in operations research giving a real-world example.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.publisherUniversitat de Barcelona
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshExperimental design
dc.subject.otherParameter Fine-tuning of Metaheuristics
dc.subject.otherDesign of Experiments
dc.subject.otherRegression Models
dc.subject.otherMulti-Objective Optimization
dc.titleStatistical methods for parameter fine-tuning of metaheuristics
dc.typeMaster thesis
dc.subject.lemacDisseny d'experiments
dc.subject.amsClassificació AMS::62 Statistics::62K Design of experiments
dc.identifier.slugFME-1046
dc.rights.accessOpen Access
dc.date.updated2014-07-02T04:30:21Z
dc.audience.educationlevelMàster
dc.audience.mediatorUniversitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística
dc.audience.degreeMÀSTER UNIVERSITARI EN ESTADÍSTICA I INVESTIGACIÓ OPERATIVA (Pla 2013)


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