Evolving Design Modifiers

• Published in: 2022 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 1052 - 1058
• Main Authors: Hickinbotham, Simon, Dubey, Rahul, Friel, Imelda, Colligan, Andrew, Price, Mark, Tyrrell, Andy
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.12.2022
• Subjects: Biology; evodevo; Evolutionary computation; Extraterrestrial measurements; generative design; Genetic algorithms; Iterative algorithms; neural networks; structural engineering
• DOI: 10.1109/SSCI51031.2022.10022087

Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.