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Topics in Evolutionary Computation and Model-Based Clustering

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dc.contributor.advisor Ashlock, Daniel
dc.contributor.advisor Browne, Ryan
dc.contributor.author McNicholas, Sharon
dc.date.accessioned 2016-05-02T14:30:42Z
dc.date.available 2016-05-02T14:30:42Z
dc.date.copyright 2016-04
dc.date.created 2016-04-20
dc.date.issued 2016-05-02
dc.identifier.uri http://hdl.handle.net/10214/9613
dc.description.abstract Topics in evolutionary computation and mixture model-based clustering are explored, as well as some work at the intersection of the two. First, complex fitness landscapes and evolutionary computation techniques are explored using cellular automata, and single parent techniques are developed to improve evolutionary techniques for locating and generalizing automata rules. Next, mixture model-based approaches to clustering are considered. Parameter estimation in mixture model-based approaches to statistical learning is notoriously difficult, and an evolutionary computation approach is proposed and illustrated on the well-established Gaussian mixture model. Next, a mixture of variance-gamma factor analyzers model is developed to facilitate the flexible clustering of high-dimensional data. This thesis concludes with a discussion and suggestions for future work. en_US
dc.description.sponsorship NSERC Alexander Graham Bell scholarship (CGS D) en_US
dc.language.iso en en_US
dc.subject Statistics en_US
dc.subject Evolutionary Computation en_US
dc.subject Cellular Automata en_US
dc.subject Cluster Analysis en_US
dc.subject Variance-Gamma en_US
dc.subject Mixture Model en_US
dc.subject Factor Analysis en_US
dc.title Topics in Evolutionary Computation and Model-Based Clustering en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Doctor of Philosophy en_US
dc.degree.department Department of Mathematics and Statistics en_US
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