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Topological reasoning using a generative representation and a genetic algorithm

Zhang, Yu 2009. Topological reasoning using a generative representation and a genetic algorithm. PhD Thesis, Cardiff University.

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Abstract

This thesis studies the use of a generative representation with a genetic algorithm (GA) to solve topological reasoning problems. Literature review indicates that generative representations outperform the non-generative ones for certain design optimisation and automation problems. However, it also indicates a lack of understanding of this relatively new class of representations. Many problems and questions about the implementation of generative representations are still to be addressed and answered. The results and findings presented in this thesis contribute to the knowledge of generative representations by: 1. explaining why genotype formatting is important for the representation and how it influences the performance of both the representation and the algorithm 2. providing different crossover and mutation methods, including both existing and newly developed ones, that are available to GA when used with the presentation and, more importantly, revealing their different properties in generating new individuals 3. providing alternative ways to map turtle graphs into the design space to form the actual designs and showing the properties of these different mapping methods and how they influence the outcome of the search. In general, this thesis examines the key issues in setting up and implementing generative representations with genetic algorithms. It improves the understanding of generative representations and contributes to the knowledge that is required to further develop them for real-world use. Based on the results and findings of this study, directions for future work are also provided.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
ISBN: 9781303218415
Date of First Compliant Deposit: 30 March 2016
Last Modified: 19 Mar 2016 23:31
URI: http://orca.cf.ac.uk/id/eprint/54999

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