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WHAT IS GENERATIVE IN GENERATIVE DESIGN TOOLS? UNCOVERING TOPOLOGICAL GENERATIVITY WITH A C-K MODEL OF EVOLUTIONARY ALGORITHMS

Published online by Cambridge University Press:  27 July 2021

Armand Hatchuel ,
Pascal Le Masson ,
Maxime Thomas  and
Benoit Weil
Armand Hatchuel
Affiliation:
MINES ParisTech-PSL
Pascal Le Masson*
Affiliation:
MINES ParisTech-PSL
Maxime Thomas
Affiliation:
MINES ParisTech-PSL
Benoit Weil
Affiliation:
MINES ParisTech-PSL
*
Le Masson, Pascal, MINES ParisTech-PSL Management Science France, [email protected]

Abstract

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Generative design (GD) algorithms is a fast growing field. From the point of view of Design Science, this fast growth leads to wonder what exactly is 'generated' by GD algorithms and how? In the last decades, advances in design theory enabled to establish conditions and operators that characterize design generativity. Thus, it is now possible to study GD algorithms with the lenses of Design Science in order to reach a deeper and unified understanding of their generative techniques, their differences and, if possible, find new paths for improving their generativity.

In this paper, first, we rely on C-K ttheory to build a canonical model of GD, based independent of the field of application of the algorithm. This model shows that GD is generative if and only if it builds, not one single artefact, but a “topology of artefacts” that allows for design constructability, covering strategies, and functional comparability of designs. Second, we use the canonical model to compare four well documented and most advanced types of GD algorithms. From these cases, it appears that generating a topology enables the analyses of interdependences and the design of resilience.

Keywords

C-K theorygenerative design algorithmsDesign theoryComputational design methodsDesign informatics
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3419 - 3430
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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