A quality diversity study in EvoDevo processes for engineering design

Edgar Buchanan Berumen, Simon John Hickinbotham, Rahul Dubey, Imelda Friel, Andrew Colligan, Mark Price, Andy Tyrrell

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationIEEE World Congress on Computational Intelligence 2024
Publication statusAccepted/In press - 15 Mar 2024
EventIEEE World Congress on Computational Intelligence 2024 - Yokohama, Japan
Duration: 30 Jun 20245 Jul 2024


ConferenceIEEE World Congress on Computational Intelligence 2024
Abbreviated titleIEEE WCCI 2024

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.


  • EvoDevo
  • Generative Design
  • structural engineering
  • quality diversity
  • Neural networks

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