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Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
Dissertation (MSc (Computer Science))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613707639455744 |
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| access_status_str | Open Access |
| author2 | Bosman, Anna |
| author_browse | Bosman, Anna |
| author_facet | Bosman, Anna |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Computer Science))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89677 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:25.890Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/89677 Training Feedforward Neural Networks with Bayesian Hyper-Heuristics Bosman, Anna an.schreuder@up.ac.za Engelbrecht, Andries Cleghorn, Christopher Schreuder, Arné Hyper-heuristics Feedforward neural networks Bayesian statistics Meta-learning Supervised learning UCTD Dissertation (MSc (Computer Science))--University of Pretoria, 2023. Many different heuristics have been developed and used to train feedforward neural networks (FFNNs). However, selection of the best heuristic to train FFNNs is a time consuming and non-trivial exercise. Careful, systematic selection is required to ensure that the best heuristic is used to train FFNNs. In the past, selection was done by trial and error. A modern approach is to automate the heuristic selection process. Often it is found that a single approach is not sufficient. Research has proposed the use of hybridisation of heuristics. One such approach is referred to as hyper-heuristics (HHs). HHs focus on dynamically finding the best heuristic or combinations of heuristics in heuristic-space by making use of heuristic performance information. One such implementation of a HH is a population-based approach that guides the search process by dynamically selecting heuristics from a heuristic-pool to be applied to different entities that represent candidate solutions to the problem-space, and work together to find good solutions. This dissertation introduces a novel population-based Bayesian hyper-heuristic (BHH). An empirical study is done by using the BHH to train FFNNs. An in-depth behaviour analysis is done and the performance of the BHH is compared to that of ten popular low-level heuristics each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics. The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. Results are analysed for statistical significance and the BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process. Computer Science MSc (Computer Science) Unrestricted 2023-02-17T12:40:45Z 2023-02-17T12:40:45Z 2023-04 2023 Dissertation * A2023 https://repository.up.ac.za/handle/2263/89677 10.25403/UPresearchdata.22116878 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Hyper-heuristics Feedforward neural networks Bayesian statistics Meta-learning Supervised learning UCTD Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title | Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title_full | Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title_fullStr | Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title_full_unstemmed | Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title_short | Training Feedforward Neural Networks with Bayesian Hyper-Heuristics |
| title_sort | training feedforward neural networks with bayesian hyper heuristics |
| topic | Hyper-heuristics Feedforward neural networks Bayesian statistics Meta-learning Supervised learning UCTD |
| url | https://repository.up.ac.za/handle/2263/89677 |