Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T18:12:52.209Z Has data issue: false hasContentIssue false
  • English
  • Français

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, pascal.le_masson@mines-paristech.fr

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Bernal, M., Haymaker, J. R. & Eastman, C. 2015. On the role of computational support for designers in action. Design Studies, 41, 163–182.10.1016/j.destud.2015.08.001CrossRefGoogle Scholar
Bossens, D. M., Mouret, J.-B. & Tarapore, D. Learning behaviour-performance maps with meta-evolution. GECCO'20 - Genetic and Evolutionary Computation Conf, 2020-07-08 2020 Cancun, Mexico.Google Scholar
Buonamici, F., Carfagni, M., Furferi, R., Governi, L. & Bvople, Y. 2020. Generative Design: An Explorative Study. Computer-Aided Design and Applications, 18, 144–155.10.14733/cadaps.2021.144-155CrossRefGoogle Scholar
Burnap, A., Liu, Y., Pan, Y., Lee, H., Gonzalez, R. & Papalambros, P. Y. Estimating and Exploring the Product Form Design Space Using Deep Generative Models. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference10.1115/DETC2016-60091CrossRefGoogle Scholar
Byrne, J., Cardiff, P., Brabazon, A. & O׳neill, M. 2014. Evolving parametric aircraft models for design exploration and optimisation. Neurocomputing, 142, 39–47.10.1016/j.neucom.2014.04.004CrossRefGoogle Scholar
CAETANO, I., Santos, L. & Leitão, A. 2020. Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9, 287–300.10.1016/j.foar.2019.12.008CrossRefGoogle Scholar
Chaszar, A. & Joyce, S. C. 2016. Generating freedom: Questions of flexibility in digital design and architectural computation. International Journal of Architectural Computing, 14, 167–181.10.1177/1478077116638945CrossRefGoogle Scholar
Chen, X. A., Tao, Y., Wang, G., Kang, R., Grossman, T., Coros, S. & Hudson, S. E. 2018. Forte: User-Driven Generative Design. Proceedings of the 2018 CHI Conference Montreal QC, Canada: ACM.Google Scholar
Cully, A., Clune, J., Tarapore, D. & MOURET, J.-B. 2015. Robots that can adapt like animals. Nature, 521, 503–507.10.1038/nature14422CrossRefGoogle ScholarPubMed
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.10.1109/4235.996017CrossRefGoogle Scholar
Draguljić, D., Santner, T. J. & Dean, A. M. 2012. Noncollapsing Space-Filling Designs for Bounded Nonrectangular Regions. Technometrics, 54, 169–178.10.1080/00401706.2012.676951CrossRefGoogle Scholar
Fontaine, M. C. Lee, S., Soros, L. B., Silva, F. D. M., Togelius, J. & Hoover, A. K. 2019. Mapping hearthstone deck spaces through MAP-elites with sliding boundaries. Proceedings of the Genetic and Evolutionary Computation Conference. Prague, Czech Republic: ACMGoogle Scholar
Gaier, A., Asteroth, A. & MOURET, J.-B. Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination (GECCO 2017), 2017 Berlin, Germany.Google Scholar
Hatchuel, A., Le Masson, P., Reich, Y. & Subrahmanian, E. 2018. Design theory: a foundation of a new paradigm for design science and engineering. Research in Engineering Design, 29, 5–21.10.1007/s00163-017-0275-2CrossRefGoogle Scholar
Hatchuel, A., Le Masson, P., Reich, Y. & Weil, B. A systematic approach of design theories using generativeness and robustness. ICED, 2011 Copenhagen, Technical University of Denmark. 12.Google Scholar
Hatchuel, A., Le masson, P., Weil, B. & Carvajal Perez, D. Innovative design within tradition - injecting topos structures in C-K theory to model culinary creation heritage, 2019 Delft, Netherlands.Google Scholar
Hatchuel, A. & Weil, B. 2009. C-K design theory: an advanced formulation. Research in Engineering Design, 19, 181–192.10.1007/s00163-008-0043-4CrossRefGoogle Scholar
Kazi, R. H., Grossman, T., Cheong, H., Hashemi, A. & Fitzmaurice, G. 2017. DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design. Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. Québec City, QC, Canada.10.1145/3126594.3126662CrossRefGoogle Scholar
Khan, S. & Awan, M. J. 2018. A generative design technique for exploring shape variations. Advanced Engineering Informatics, 38, 712–724.10.1016/j.aei.2018.10.005CrossRefGoogle Scholar
Koos, S., Cully, A. & MOURET, J.-B. 2013. Fast Damage Recovery in Robotics with the T-Resilience Algorithm. ArXiv.10.1177/0278364913499192CrossRefGoogle Scholar
Kroll, E., Le masson, P. & Weil, B. 2014. Steepest-first exploration with learning-based path evaluation. Research in Engineering Design, 25, 351–373.10.1007/s00163-014-0182-8CrossRefGoogle Scholar
Le Masson, P., Hatchuel, A. & Weil, B. 2016. Design Theory at Bauhaus: teaching “splitting” knowledge. Research in Engineering Design, 27, 91–115.10.1007/s00163-015-0206-zCrossRefGoogle Scholar
Lehman, J. & Stanley, K. 2011a. Evolving a diversity of creatures through novelty search and local competition, GECCO'1110.1145/2001576.2001606CrossRefGoogle Scholar
Lehman, J. & Stanley, K. O. 2011b. Abandoning objectives: Evolution through the search for novelty alone. Evol. Comput., 19, 189–223.10.1162/EVCO_a_00025CrossRefGoogle Scholar
Matejka, J., Glueck, M., Bradner, E., Hashemi, A., Grossman, T. & Fitzmaurice, G. 2018. Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Montreal QC, Canada: ACM.10.1145/3173574.3173943CrossRefGoogle Scholar
Mccormack, J., Dorin, A. & Innocent, T. Generative design: a paradigm for design research. 2005.Google Scholar
Mountstephens, J. & Teo, J. 2020. Progress and Challenges in Generative Product Design: A Review of Systems. Computers, 9, 4.10.3390/computers9040080CrossRefGoogle Scholar
Mouret, J.-B. & Clune, , J. 2015. Illuminating search spaces by mapping elites. ArXiv.Google Scholar
Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., Zhao, D. & Benjamin, D. 2017. Project discover: an application of generative design for architectural space planning. Proceedings of the Symposium on Simulation for Architecture and Urban Design. Toronto, Canada:Google Scholar
Nguyen, A., Yosinski, J. & Clune, J. Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. Proc. of the Genetic and Evolutionary Computation Conference. 2015.10.1145/2739480.2754703CrossRefGoogle Scholar
Oh, S., Jung, Y., Kim, S., Lee, I. & Kang, N. 2019. Deep Generative Design: Integration of Topology Optimization and Generative Models. arXiv: Learning.Google Scholar
Pugh, J. Soros, K. L. B. & Stanley, K. 2016. Quality Diversity: A New Frontier for Evolutionary Computation. Frontiers Robotics AI, 3, 40.10.3389/frobt.2016.00040CrossRefGoogle Scholar
Reich, Y., Hatchuel, A., Shai, O. & Subrahmanian, E. 2012. A Theoretical Analysis of Creativity Methods in Engineering Design: Casting ASIT within C-K Theory Journal of Eng. Design, 23, 137–158.Google Scholar
Shea, K., Aish, R. & Gourtovaia, M. 2005. Towards integrated performance-driven generative design tools. Automation in Construction, 14, 253–264.10.1016/j.autcon.2004.07.002CrossRefGoogle Scholar
Umetani, N. 2017. Exploring generative 3D shapes using autoencoder networks. SIGGRAPH Asia 2017 Technical Briefs. Bangkok, Thailand: Association for Computing Machinery.Google Scholar
Woolley, B. G. & Stanley, K. O. On the Deleterious Effects of A Priori Objectives on Evolution and Representation. Proceedings of the Genetic and Evolutionary Computation Conference., 2011.Google Scholar
WÌnsch, A., Schabacker, M. & Vajna, S. 2012. Designing a gearbox for a novel independently controllable transmission using autogenetic design theory. 9th International Workshop on Integrated Product Development. Magdeburg, Germany.Google Scholar