An adaptive encoding of geometric shapes suitable for aerodynamic design using genetic algorithms
An adaptive encoding of geometric shapes suitable for aerodynamic design using genetic algorithms
Natural evolutionary systems exhibit a complex mapping between the genetic encoding carried by cells, to the body an form of a living species. The nature of this mapping facilitates the hereditary transfer of parental features to offspring through genes. Adaptation to this mapping occurs during the reproduction process, when parental chromosomes are blended together, and random mutations creep into this process.
Genetic Algorithms, which mimic evolutionary processes such as natural selection, reproduction , and survival of the fittest, can be applied to the problem of aerodynamic design, by breeding shapes together in the hope of finding better ones. Fixed chromosome structures are currently used to map the genetic encoding adapted by the Genetic Algorithm, to a geometric language that can be used to describe shapes such as airfoil sections or wings. To adequately encapsulate high quality aerodynamic shapes, large numbers of genes are required by this mapping at significant expense to the evolutionary process.
Suitable methods that reduce the computational time required to evolve aerodynamic shapes, may be sought by using an encoding that can add necessary detail to shapes, and adapting the complexity of its description.
In this thesis, the complexity and adaptation of shape encoding is explored. A distributed Genetic Algorithm has been created over clusters of networked PCs to perform aerodynamic optimisation. Different representations for describing shapes have been used to design airfoil sections. In order to reduce computational cost, meta-modelling techniques were successfully implemented to predict which newly created shapes will be useful to the Genetic Algorithm, repairing breeding errors to increase design survivability. An object orientated chromosome framework has been developed, to facilitate adaptation of both genes and chromosome structure by Genetic Algorithms. A new hierarchical crossover operator is explored on evolving simple curves from straight lines, by adapting the complexity of the chromosome mapping used by Genetic Algorithm. Finally, the new adaptive encoding is exploited to evolve aerofoil sections, resulting in improvements to design quality and performance costs.
University of Southampton
Law, Robert Andrew
e0210490-0886-4941-a086-e6df66200d6b
2002
Law, Robert Andrew
e0210490-0886-4941-a086-e6df66200d6b
Law, Robert Andrew
(2002)
An adaptive encoding of geometric shapes suitable for aerodynamic design using genetic algorithms.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
Natural evolutionary systems exhibit a complex mapping between the genetic encoding carried by cells, to the body an form of a living species. The nature of this mapping facilitates the hereditary transfer of parental features to offspring through genes. Adaptation to this mapping occurs during the reproduction process, when parental chromosomes are blended together, and random mutations creep into this process.
Genetic Algorithms, which mimic evolutionary processes such as natural selection, reproduction , and survival of the fittest, can be applied to the problem of aerodynamic design, by breeding shapes together in the hope of finding better ones. Fixed chromosome structures are currently used to map the genetic encoding adapted by the Genetic Algorithm, to a geometric language that can be used to describe shapes such as airfoil sections or wings. To adequately encapsulate high quality aerodynamic shapes, large numbers of genes are required by this mapping at significant expense to the evolutionary process.
Suitable methods that reduce the computational time required to evolve aerodynamic shapes, may be sought by using an encoding that can add necessary detail to shapes, and adapting the complexity of its description.
In this thesis, the complexity and adaptation of shape encoding is explored. A distributed Genetic Algorithm has been created over clusters of networked PCs to perform aerodynamic optimisation. Different representations for describing shapes have been used to design airfoil sections. In order to reduce computational cost, meta-modelling techniques were successfully implemented to predict which newly created shapes will be useful to the Genetic Algorithm, repairing breeding errors to increase design survivability. An object orientated chromosome framework has been developed, to facilitate adaptation of both genes and chromosome structure by Genetic Algorithms. A new hierarchical crossover operator is explored on evolving simple curves from straight lines, by adapting the complexity of the chromosome mapping used by Genetic Algorithm. Finally, the new adaptive encoding is exploited to evolve aerofoil sections, resulting in improvements to design quality and performance costs.
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Published date: 2002
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Local EPrints ID: 465097
URI: http://eprints.soton.ac.uk/id/eprint/465097
PURE UUID: 347ee9c2-7bc5-4f7a-9850-802aeccb687d
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Date deposited: 05 Jul 2022 00:23
Last modified: 16 Mar 2024 19:57
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Author:
Robert Andrew Law
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