Buchanan Berumen, Edgar orcid.org/0000-0001-6587-8808, Hickinbotham, Simon John orcid.org/0000-0003-0880-4460, Dubey, Rahul orcid.org/0000-0003-1524-7797 et al. (4 more authors) (2024) A quality diversity study in EvoDevo processes for engineering design. In: IEEE World Congress on Computational Intelligence 2024. IEEE World Congress on Computational Intelligence 2024, 30 Jun - 05 Jul 2024 IEEE , JPN
Abstract
For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | EvoDevo,Generative Design,structural engineering,quality diversity,Neural networks |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/V007335/1 |
Depositing User: | Pure (York) |
Date Deposited: | 22 Mar 2024 16:20 |
Last Modified: | 05 Feb 2025 14:10 |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210793 |