Morphogenic Shape Grammars for the Design of Engineering Structures

Bodies of multicellular organisms are laid out according to morphogens: chemical agents which establish a coordinate system in the early embryo and use this to decide where body parts should grow. This process offers a mechanism for the automation of the design of engineering constructs via evolutio...

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Bibliographic Details
Published in2025 IEEE Symposium on Computational Intelligence on Engineering/Cyber Physical Systems (CIES) pp. 1 - 6
Main Authors Hickinbotham, Simon, Buchanan, Edgar, Kilpatrick, Peter, Price, Mark, Tyrrell, Andy M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.03.2025
Subjects
Bridges
Chemicals
Evolution (biology)
Evolutionary computation
Generative AI
Grammar
Organisms
Search problems
Shape
Structural engineering
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DOI10.1109/CIES64955.2025.11007633

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Summary:Bodies of multicellular organisms are laid out according to morphogens: chemical agents which establish a coordinate system in the early embryo and use this to decide where body parts should grow. This process offers a mechanism for the automation of the design of engineering constructs via evolutionary search in a similar manner to the way biological evolution has driven the diversity of body forms of life on Earth. There are many ways of encoding such body plans, but the main existing approaches have problems around managing the complexity and stability of the evolutionary search process, particularly when applied to practical engineering design problems. This contribution takes the notion of morphogen chemical gradients and uses it to develop a novel grammar for shape formation. The central idea is to organise and label the spatial sub-regions of a design before making decisions regarding the finished arrangement of structure. This makes it much simpler to explore compositions of the hierarchy of sub-assemblies in a design, and to represent design of shape in an evolvable manner. A worked example of such a morphogenic shape grammar is described, and used in a multi-objective evolutionary search for optimal bridge truss structures over four fitness objectives with two design constraints. The resulting Pareto front shows a wide variety of bridge designs, demonstrating the power of this approach to generate a diverse set of viable options to meet engineering design challenges.
DOI:10.1109/CIES64955.2025.11007633