A Quality Diversity Study in EvoDevo Processes for Engineering Design
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.
Department(s)
Computer Science
Document Type
Conference Proceeding
DOI
10.1109/CEC60901.2024.10612076
Keywords
evodevo, generative design, neural networks, quality diversity, structural engineering
Publication Date
1-1-2024
Recommended Citation
Dubey, Rahul; Buchanan, Edgar; Hickinbotham, Simon; Friel, Imelda; Colligan, Andrew; Price, Mark; and Tyrrell, Andy M., "A Quality Diversity Study in EvoDevo Processes for Engineering Design" (2024). Faculty Scholarship. 488.
https://bearworks.missouristate.edu/articles00/488
Journal Title
2024 IEEE Congress on Evolutionary Computation CEC 2024 Proceedings
