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dc.contributor.authorDennis, Cody
dc.date.accessioned2021-12-02T15:28:00Z
dc.date.available2021-12-02T15:28:00Z
dc.identifier.urihttp://hdl.handle.net/10464/15482
dc.description.abstractThe issue of parameter selection cannot be ignored if optimal performance is to be obtained from an algorithm on a specific problem or if a collection of algorithms are going to be compared in a fair manner. Unfortunately, adequately addressing the issue of parameter selection is time consuming and computationally expensive. Searching for appropriate control parameters generally requires much more time than actually solving the problem at hand due to the need to perform many complete runs of the target algorithm. The number of runs required to obtain thorough and equal coverage of the parameter space grows exponentially with the number of parameters. As a result, costs associated with parameter selection become a limiting factor in the scale of problems that can be investigated. The primary goal of this work is to reduce the costs of parameter selection. In pursuit of this goal, this thesis examines the use of neural networks to intelligently select appropriate control parameter values based on the characteristics of the problem at hand. Two general purpose approaches are evaluated: one that predicts a single set of control parameters to use throughout a run of the target algorithm; and, another that dynamically adjusts algorithm control parameters at run time. These approaches are examined in detail using the Particle Swarm Optimization algorithm. A comparison with state of the art automated tools for control parameter selection indicates that the cost of parameter selection can be significantly reduced.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectFitness Landscape Analysisen_US
dc.subjectAlgorithm Configurationen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectParameter Tuningen_US
dc.subjectParameter Controlen_US
dc.titleLandscape Aware Algorithm Configurationen_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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