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Multi-objective evolutionary algorithms for product design
Identifying chemical compounds with optimal properties for specific applications presents a fundamental challenge in materials science. Traditional methods, based on trialand-error, are inefficient and costly. This thesis introduces an innovative integration of Computational Chemistry and Machine Le...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2024
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| _version_ | 1867613178949533696 |
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| access_status_str | Open Access |
| author | Aslan, Bilal Hasan |
| author2 | Nitschke, Geoff |
| author_browse | Aslan, Bilal Hasan Nitschke, Geoff |
| author_facet | Nitschke, Geoff Aslan, Bilal Hasan |
| author_sort | Aslan, Bilal Hasan |
| collection | Thesis |
| description | Identifying chemical compounds with optimal properties for specific applications presents a fundamental challenge in materials science. Traditional methods, based on trialand-error, are inefficient and costly. This thesis introduces an innovative integration of Computational Chemistry and Machine Learning (ML) with Evolutionary MultiObjective Optimisation (EMOO) techniques to streamline compound design. This approach automates the design process by leveraging ML to accurately predict compound properties and using EMOO to select compounds that meet various criteria. The significance of this work lies in its potential to transform the traditional development process, facilitating the creation of chemical products that fulfill multiple objectives more efficiently. This study not only demonstrates the synergy between advanced ML and optimisation techniques but also presents a comprehensive comparison of the MultiObjective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) and Nondominated Sorting Genetic Algorithm II (NSGA-II), including two novel meta-heuristics for enhanced molecular exploration. Our findings reveal that MO-CMA-ES, especially when combined with an extended search meta-heuristic, excels in exploring molecular spaces, establishing it as a preferred method for compound synthesis. This research promises to accelerate compound development specifically for detergent compounds, offering significant implications for product design across various industries. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40768 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:00.945Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40768 Multi-objective evolutionary algorithms for product design Aslan, Bilal Hasan Nitschke, Geoff Computer Science Identifying chemical compounds with optimal properties for specific applications presents a fundamental challenge in materials science. Traditional methods, based on trialand-error, are inefficient and costly. This thesis introduces an innovative integration of Computational Chemistry and Machine Learning (ML) with Evolutionary MultiObjective Optimisation (EMOO) techniques to streamline compound design. This approach automates the design process by leveraging ML to accurately predict compound properties and using EMOO to select compounds that meet various criteria. The significance of this work lies in its potential to transform the traditional development process, facilitating the creation of chemical products that fulfill multiple objectives more efficiently. This study not only demonstrates the synergy between advanced ML and optimisation techniques but also presents a comprehensive comparison of the MultiObjective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) and Nondominated Sorting Genetic Algorithm II (NSGA-II), including two novel meta-heuristics for enhanced molecular exploration. Our findings reveal that MO-CMA-ES, especially when combined with an extended search meta-heuristic, excels in exploring molecular spaces, establishing it as a preferred method for compound synthesis. This research promises to accelerate compound development specifically for detergent compounds, offering significant implications for product design across various industries. 2024-12-04T09:27:56Z 2024-12-04T09:27:56Z 2024 2024-12-04T09:24:31Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40768 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Aslan, Bilal Hasan Multi-objective evolutionary algorithms for product design |
| thesis_degree_str | Master's |
| title | Multi-objective evolutionary algorithms for product design |
| title_full | Multi-objective evolutionary algorithms for product design |
| title_fullStr | Multi-objective evolutionary algorithms for product design |
| title_full_unstemmed | Multi-objective evolutionary algorithms for product design |
| title_short | Multi-objective evolutionary algorithms for product design |
| title_sort | multi objective evolutionary algorithms for product design |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/40768 |
| work_keys_str_mv | AT aslanbilalhasan multiobjectiveevolutionaryalgorithmsforproductdesign |