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dc.contributor.authorMcLean, Reginald
dc.date.accessioned2019-11-06T14:25:51Z
dc.date.available2019-11-06T14:25:51Z
dc.identifier.urihttp://hdl.handle.net/10464/14563
dc.description.abstractThe main focus of this thesis is to compare the ability of various swarm intelligence algorithms when applied to the training of artificial neural networks. In order to compare the performance of the selected swarm intelligence algorithms both classification and regression datasets were chosen from the UCI Machine Learning repository. Swarm intelligence algorithms are compared in terms of training loss, training accuracy, testing loss, testing accuracy, hidden unit saturation, and overfitting. Our observations showed that Particle Swarm Optimization (PSO) was the best performing algorithm in terms of Training loss and Training accuracy. However, it was also found that the performance of PSO dropped considerably when examining the testing loss and testing accuracy results. For the classification problems, it was found that firefly algorithm, ant colony optimization, and fish school search outperformed PSO for testing loss and testing accuracy. It was also observed that ant colony optimization was the algorithm that performed the best in terms of hidden unit saturation.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectneural network swarm-intelligence saturation overfittingen_US
dc.titleSwarm-based Algorithms for Neural Network Trainingen_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
refterms.dateFOA2021-08-12T01:48:28Z


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