Multi-objective evolutionary algorithms for product design
dc.contributor.advisor | Nitschke, Geoff | |
dc.contributor.author | Aslan, Bilal Hasan | |
dc.date.accessioned | 2024-12-04T09:27:56Z | |
dc.date.available | 2024-12-04T09:27:56Z | |
dc.date.issued | 2024 | |
dc.date.updated | 2024-12-04T09:24:31Z | |
dc.description.abstract | 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. | |
dc.identifier.apacitation | Aslan, B. H. (2024). <i>Multi-objective evolutionary algorithms for product design</i>. (). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/40768 | en_ZA |
dc.identifier.chicagocitation | Aslan, Bilal Hasan. <i>"Multi-objective evolutionary algorithms for product design."</i> ., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2024. http://hdl.handle.net/11427/40768 | en_ZA |
dc.identifier.citation | Aslan, B.H. 2024. Multi-objective evolutionary algorithms for product design. . University of Cape Town ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/40768 | en_ZA |
dc.identifier.ris | TY - Thesis / Dissertation AU - Aslan, Bilal Hasan AB - 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. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Multi-objective evolutionary algorithms for product design TI - Multi-objective evolutionary algorithms for product design UR - http://hdl.handle.net/11427/40768 ER - | en_ZA |
dc.identifier.uri | http://hdl.handle.net/11427/40768 | |
dc.identifier.vancouvercitation | Aslan BH. Multi-objective evolutionary algorithms for product design. []. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40768 | en_ZA |
dc.language.rfc3066 | eng | |
dc.publisher.department | Department of Computer Science | |
dc.publisher.faculty | Faculty of Science | |
dc.publisher.institution | University of Cape Town | |
dc.subject | Computer Science | |
dc.title | Multi-objective evolutionary algorithms for product design | |
dc.type | Thesis / Dissertation | |
dc.type.qualificationlevel | Masters | |
dc.type.qualificationlevel | MSc |