Investigation of starting conditions in generative processes for the design of engineering structures

Edgar Buchanan*, Rahul Dubey, Simon Hickinbotham, Imelda Friel, Andrew Colligan, Mark Price, Andy M. Tyrrell

*Corresponding author for this work

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

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Engineering design has traditionally involved human engineers manually creating and iterating on designs based on their expertise and knowledge. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are used to ex-plore a much larger design space that may not have ever been considered by human engineers. However, for complex systems, the designer is often required to start the EvoDevo process with an initial design (seed) which the development process will optimise. The question is: will a good starting seed yield a good set of design solutions for the given problem? This paper considers this question and suggests that sub-optimal seeds can provide, up to certain limits, better design solutions than relatively more optimal seeds. In addition, this paper highlights the importance of designing the appropriate seed for the appropriate problem. In this paper, the problem analysed is the structural performance of a Warren Truss (bridge-like structure) under a single load. The main conclusion of this paper is that up to a limit sub-optimal seeds provide in general better sets of solutions than more optimal seeds. After this limit, the performance of sub-optimal seed starts to degrade as parts of the phenotype landscape become inaccessible.

Original languageEnglish
Title of host publicationProceedings of the IEEE Symposium Series on Computational Intelligence, SSCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665430654
ISBN (Print)9781665430647
Publication statusPublished - 01 Jan 2024
Event2023 IEEE Symposium Series on Computational Intelligence - Mexico City, Mexico
Duration: 05 Dec 202308 Dec 2023

Publication series

NameIEEE Symposium Series on Computational Intelligence: Proceedings
ISSN (Print)2770-0097
ISSN (Electronic)2472-8322


Conference2023 IEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI 2023
CityMexico City


  • Generative design
  • Genetic algorithms
  • Neural Networks
  • Structural engineering


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