Multi-objective evolutionary algorithms for product design

dc.contributor.advisorNitschke, Geoff
dc.contributor.authorAslan, Bilal Hasan
dc.date.accessioned2024-12-04T09:27:56Z
dc.date.available2024-12-04T09:27:56Z
dc.date.issued2024
dc.date.updated2024-12-04T09:24:31Z
dc.description.abstractIdentifying 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.apacitationAslan, 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/11427/40768en_ZA
dc.identifier.chicagocitationAslan, 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/11427/40768en_ZA
dc.identifier.citationAslan, 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/11427/40768en_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.urihttp://hdl.handle.net/11427/40768
dc.identifier.vancouvercitationAslan 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/11427/40768en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Computer Science
dc.publisher.facultyFaculty of Science
dc.publisher.institutionUniversity of Cape Town
dc.subjectComputer Science
dc.titleMulti-objective evolutionary algorithms for product design
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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