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dc.contributor.advisorMiguel, Ian
dc.contributor.advisorGent, Ian G.
dc.contributor.authorKotthoff, Lars
dc.coverage.spatial192en_US
dc.date.accessioned2012-06-22T15:15:52Z
dc.date.available2012-06-22T15:15:52Z
dc.date.issued2012-06-20
dc.identifieruk.bl.ethos.554187
dc.identifier.urihttp://hdl.handle.net/10023/2841
dc.description.abstractThe Algorithm Selection Problem is to select the most appropriate way for solving a problem given a choice of different ways. Some of the most prominent and successful applications come from Artificial Intelligence and in particular combinatorial search problems. Machine Learning has established itself as the de facto way of tackling the Algorithm Selection Problem. Yet even after a decade of intensive research, there are no established guidelines as to what kind of Machine Learning to use and how. This dissertation presents an overview of the field of Algorithm Selection and associated research and highlights the fundamental questions left open and problems facing practitioners. In a series of case studies, it underlines the difficulty of doing Algorithm Selection in practice and tackles issues related to this. The case studies apply Algorithm Selection techniques to new problem domains and show how to achieve significant performance improvements. Lazy learning in constraint solving and the implementation of the alldifferent constraint are the areas in which we improve on the performance of current state of the art systems. The case studies furthermore provide empirical evidence for the effectiveness of using the misclassification penalty as an input to Machine Learning. After having established the difficulty, we present an effective technique for reducing it. Machine Learning ensembles are a way of reducing the background knowledge and experimentation required from the researcher while increasing the robustness of the system. Ensembles do not only decrease the difficulty, but can also increase the performance of Algorithm Selection systems. They are used to much the same ends in Machine Learning itself. We finally tackle one of the great remaining challenges of Algorithm Selection -- which Machine Learning technique to use in practice. Through a large-scale empirical evaluation on diverse data taken from Algorithm Selection applications in the literature, we establish recommendations for Machine Learning algorithms that are likely to perform well in Algorithm Selection for combinatorial search problems. The recommendations are based on strong empirical evidence and additional statistical simulations. The research presented in this dissertation significantly reduces the knowledge threshold for researchers who want to perform Algorithm Selection in practice. It makes major contributions to the field of Algorithm Selection by investigating fundamental issues that have been largely ignored by the research community so far.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.rightsCreative Commons Attribution-ShareAlike 3.0 Unported
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/
dc.subjectAlgorithm selectionen_US
dc.subjectCombinatorial searchen_US
dc.subjectConstraint programmingen_US
dc.subjectSatisfiabilityen_US
dc.subjectMachine learningen_US
dc.subject.lccQ325.5K78
dc.subject.lcshAlgorithmsen_US
dc.subject.lcshMachine learningen_US
dc.subject.lcshCombinatorial analysisen_US
dc.subject.lcshConstraint programming (Computer science)en_US
dc.subject.lcshConstraints (Artificial intelligence)en_US
dc.titleOn algorithm selection, with an application to combinatorial search problemsen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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Creative Commons Attribution-ShareAlike 3.0 Unported
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