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dc.contributor.authorMedland, Michael
dc.contributor.authorMedland, Michael
dc.date.accessioned2015-03-27T16:45:32Z
dc.date.available2015-03-27T16:45:32Z
dc.identifier.urihttp://hdl.handle.net/10464/6207
dc.description.abstractComplex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectGraph Modelsen_US
dc.subjectGenetic Programmingen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectComplex Networksen_US
dc.titleObject-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networksen_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


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