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dc.contributor.authorBailey, Alexander
dc.date.accessioned2013-07-26T15:36:50Z
dc.date.available2013-07-26T15:36:50Z
dc.date.issued2013-07-26
dc.identifier.urihttp://hdl.handle.net/10464/4719
dc.description.abstractComplex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectGenetic Programmingen_US
dc.subjectComplex Networksen_US
dc.subjectGraph Modelsen_US
dc.subjectAutomatic Inferenceen_US
dc.subjectProgram Synthesisen_US
dc.titleAutomatic Inference of Graph Models for Complex Networks with Genetic Programmingen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
dc.embargo.termsNoneen_US


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