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dc.contributor.authorHarrington, Adrian
dc.date.accessioned2012-11-07T19:33:49Z
dc.date.available2012-11-07T19:33:49Z
dc.date.issued2012-11-07
dc.identifier.urihttp://hdl.handle.net/10464/4136
dc.description.abstractAs the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectGenetic Programmingen_US
dc.subjectEvolutionary Designen_US
dc.subjectGenerative Representationsen_US
dc.subjectArtificial Architectureen_US
dc.subjectEvolutionary Algorithmsen_US
dc.titleEnabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architectureen_US
dc.typeElectronic Thesis or Dissertationen
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
refterms.dateFOA2021-08-08T02:22:42Z


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