Multi-Dimensional Evolutionary Algorithms for Training Neural Networks with Heterogeneous Architectures in Pricing of American Style Options

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dc.contributor.advisorGentle, James
dc.contributor.authorSharp, Andrew Clinton
dc.creatorSharp, Andrew Clinton
dc.date.accessioned2017-01-29T01:16:36Z
dc.date.available2017-01-29T01:16:36Z
dc.date.issued2016
dc.description.abstractA common problem in training artificial neural networks is determining the optimal network architecture for the particular problem. An ontology is presented that allows for the generalization of the multi-dimensional differential evolution (MD-DE) algorithm in training an ensemble of neural networks with heterogeneous architectures. This generalized algorithm is referred to as the multi-dimensional evolutionary algorithm (MD-EA) since it provides a framework for performing any of the evolutionary optimization algorithms such as differential evolution or genetic algorithms on neural networks with heterogeneous architectures.
dc.format.extent151 pages
dc.identifier.urihttps://hdl.handle.net/1920/10593
dc.language.isoen
dc.rightsCopyright 2016 Andrew Clinton Sharp
dc.subjectMathematics
dc.subjectAmerican Style Options
dc.subjectEvolutionary algorithms
dc.subjectHeterogeneous Neural Networks
dc.subjectMachine learning
dc.subjectOptimization
dc.titleMulti-Dimensional Evolutionary Algorithms for Training Neural Networks with Heterogeneous Architectures in Pricing of American Style Options
dc.typeDissertation
thesis.degree.disciplineComputational Sciences and Informatics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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