Applying deep convolutional neural networks to the dragon boat partition problem

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dc.contributor.authorRegnier, Brett
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorZhang, John Z.
dc.date.accessioned2021-09-16T23:59:09Z
dc.date.available2021-09-16T23:59:09Z
dc.date.issued2021
dc.degree.levelMastersen_US
dc.description.abstractWe investigate approximating the Dragon Boat Partition problem, a practical real-worldvariant of the Partition problem. A team of dragon boat participants must be partitionedwith an approximately balanced arrangement with a preferable weight difference of 0. Wepresent two approaches that capture the participant characteristics. The first approach takesa heuristic route. The second approach applies Deep Convolutional Neural Networks to theproblem, with two versions. In our 10,000 episodes per experiment, our heuristic imple-mentation had an average episode runtime of 1.84ms, an average of 7.39 steps per episode,perfect left-right approximation rate of 98.53%, perfect front-back approximation rate of89.16%, and a perfect combined approximation rate of 90.15%. Whereas our best deeplearning model has an average episode runtime of 1.23ms, an average of 4.65 steps perepisode, perfect left-right approximate rate of 98.00%, perfect front-back approximationrate of 95.13%, and a perfect combined approximation rate of 94.28%.en_US
dc.identifier.urihttps://hdl.handle.net/10133/6029
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectArtificial intelligence [0800]en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Computer science::Computer scienceen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Convolutional Neural Networksen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectOptimizationen_US
dc.subjectDeep Learningen_US
dc.subjectApproximation algorithmsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectComputer scienceen_US
dc.subjectDragon boat festivalsen_US
dc.subjectMachine learningen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectDissertations, Academicen_US
dc.titleApplying deep convolutional neural networks to the dragon boat partition problemen_US
dc.typeThesisen_US
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