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dc.contributor.advisorMalak, Richard J.
dc.creatorGalvan, Edgar
dc.date.accessioned2012-10-19T15:30:54Z
dc.date.accessioned2012-10-22T18:05:03Z
dc.date.available2012-10-19T15:30:54Z
dc.date.available2012-10-22T18:05:03Z
dc.date.created2012-08
dc.date.issued2012-10-19
dc.date.submittedAugust 2012
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11783
dc.description.abstractIt is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. Several researchers have investigated approaches for modeling the capabilities of a technology with the aim of supporting the design process. In these works, the information about the physical form is typically abstracted away. However, the efficient generation of an accurate model of technical capabilities remains a challenge. Pareto frontier based methods are often used but yield results that are of limited use for subsequent decision making and analysis. Models based on parameterized Pareto frontiers—termed Technology Characterization Models (TCMs)—are much more reusable and composable. However, there exists no efficient technique for modeling the parameterized Pareto frontier. The contribution of this thesis is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The novelty of the algorithm lies in a new concept termed predicted dominance. The proposed algorithm uses fundamental concepts from multi-objective optimization and machine learning to generate a model of the technology frontier.en
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectGenetic Algorithmen
dc.subjectCapability modelen
dc.subjectnondominated frontieren
dc.titleA Genetic Algorithm Approach for Technology Characterizationen
dc.typeThesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberJohnson, Andrew L.
dc.contributor.committeeMemberMcAdams, Daniel A.
dc.type.genrethesisen
dc.type.materialtexten


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