Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495517
Title: Agency-based Integration of Aesthetic Criteria within an Interactive Evolutionary Design Environment
Author: Machwe, Azahar Tekchand
Awarding Body: University of the West of England
Current Institution: University of the West of England, Bristol
Date of Award: 2008
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Abstract:
Traditional interactive evolutionary design systems combine auser based fitness function with an evolutionary search process. Effective integration of machine based tools, human designers and real world design processes, requires a higher level of information exchange between the user and the design system. This dual requirement of increasing the connectivity between the machine and the user as well as incorporating human preferences with machine based fitness evaluations is the main focus of this research. There are two problems in implementing the above, namely the problem of representation as well as user fatigue resulting from design evaluations. The initial work involved an integration of component-based representation, software agents and machine learning with an evolutionary programming algorithm for a relatively simple bridge design problem (the Bridge Design System) with both human and machine based evaluation. The main research contribution of the Bridge Design System was the integration of componentbased representation and the machine learning sub-system. The component-based representation addresses the problem of representation. The machine learning sub-system provides a possible solution to the user fatigue problem. The Bridge Design System was extended to tackle a more complex 3-D design problem related to Urban-Furniture design. To enhance the interactivity and usability of the system, population clustering based on solution similarity was introduced within the urban-furniture design system. The user fatigue issue was addressed further through population clustering which allowed users to work with larger population sizes than usual. Clustering also allowed the identification of features present in high performance as well as user preferred solutions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Not available Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.495517  DOI: Not available
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