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Multi-objective evolutionary neural architecture search for recurrent neural networks
Dissertation (MSc (Computer Science))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613442061369344 |
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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, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/86494 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:12.613Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| 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:2263/86494 Multi-objective evolutionary neural architecture search for recurrent neural networks Bosman, Anna r.booysen@tuks.co.za Booysen, Reinhard Artificial intelligence Machine learning Neural networks Evolutionary algorithms Architecture UCTD Dissertation (MSc (Computer Science))--University of Pretoria, 2022. Artificial neural network (ANN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of ANN architectures, and has proven to be successful in finding ANN architectures that can outperform those manually designed by human experts. It is often the case that in real world implementations of machine learning and ANNs, a reasonable trade-off is accepted for marginally reduced model accuracy in favour of lower computational resources demanded by the model. This study investigates the use of multi-objective evolutionary algorithms as an exploration strategy for NAS to evolve recurrent neural network (RNN) architectures. This allows for the consideration of the underlying computational resource requirements of the RNN models while maintaining an acceptable model performance-related objective. Additionally, methods such as weight inheritance, early stopping, and pruning of architectural unit connections during offspring generation, are investigated in the context of RNN architecture search to allow for more efficient exploration of the RNN architecture search space. Computer Science MSc (Computer Science) Unrestricted 2022-07-27T12:34:57Z 2022-07-27T12:34:57Z 2022-09-07 2022 Dissertation * S2022 https://repository.up.ac.za/handle/2263/86494 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 | Artificial intelligence Machine learning Neural networks Evolutionary algorithms Architecture UCTD Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title | Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title_full | Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title_fullStr | Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title_full_unstemmed | Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title_short | Multi-objective evolutionary neural architecture search for recurrent neural networks |
| title_sort | multi objective evolutionary neural architecture search for recurrent neural networks |
| topic | Artificial intelligence Machine learning Neural networks Evolutionary algorithms Architecture UCTD |
| url | https://repository.up.ac.za/handle/2263/86494 |