Schema of generative design as an iterative process
Samba, a piece of furniture created by Guto Requena with generative design

Generative design (GD) is an iterative design process that generates outputs that meet specified constraints to varying degrees. In a second phase, designers can then provide feedback to the generator that explores the feasible region by selecting preferred outputs or changing input parameters for future iterations. Either or both phases can be done by humans or software. One method is to use a generative adversarial network, which is a pair of neural networks. The first generates a trial output. The second provides feedback for the next iteration.[1]

Algorithmic design (AD) has a very similar meaning. When used in the computer-aided architectural design context, some researchers use it synonymously with GD, some claim that AD includes GD,[2] some consider AD as a subset of GD.[3]

The output can be items such as images, sounds, architectural models, animation, and industrial parts. It is used in design fields such as art, architecture, communication design, and product design.[4]

Computers can explore orders of magnitude more permutations, exploring the interactions of the enormous numbers of design elements in small increments.It mimics nature’s evolutionary approach to design through genetic variation and selection.[5] These techniques are available even for designers with little programming experience.[6][7] It is supported by commercially available CAD packages.[8] Tools leveraging generative design as a foundation are available.[9]

Compared with traditional top-down design approaches, generative design addresses design problems by using a bottom-up paradigm. The solution itself then evolves to a good, if not optimal, solution.[10]

Generative design involves rule definition and result analysis which are integrated with the design process.[11] By defining parameters and rules, the generative approach is able to provide optimized solution for both structural stability and aesthetics. Possible design algorithms include cellular automata, shape grammar, genetic algorithm, space syntax, and most recently, artificial neural network. Due to the high complexity of the solution generated, rule-based computational tools, such as finite element method and topology optimisation, are more preferable to evaluate and optimise the generated solution.[12] The iterative process provided by computer software enables the trial-and-error approach in design, and involves architects interfering with the optimisation process.

The software then begins iterating, changing things a bit at a time, much like random mutations try out new combinations of animal DNA, and testing it against the necessary performance targets, much like life tests its DNA mutations. Over millions of generations, the software adds a little metal here, removes a little there, and checks if the part is stronger or weaker, lighter or heavier than its predecessors.

Within a surprisingly short time (a couple of hours, if given access to high-powered cloud processing), it comes back with shapes humans could never have directly designed. But they're strikingly similar to the work of nature; where there's more stress to be dealt with, they gradually become thicker. Where there's less stress, they get thinner. Support structures waste away where they're not needed, and tend to line up with the load path. In short, they start looking weirdly bony and organic.


Architecture and Industrial Design

Generative Design is a morphogenetic process using algorithms structured as not-linear systems for endless unique and unrepeatable results performed by an idea-code, as in Nature. (C.Soddu 1992) url=


Generative design has been applied in architecture.[13] Architectural design has long been regarded as a wicked problem.[14] The advantage of using generative design as a design tool is that it does not construct fixed geometries, but take a set of design rules that can generate an infinite set of possible design solutions. The generated design solutions can be more sensitive, responsive, and adaptive to the wicked problem.

Historical precedent work includes Antoni Gaudí's Sagrada Família, which used rule-based geometrical forms for structures,[15] and Buckminster Fuller's Montreal Biosphere where the rules to generate individual components is designed, rather than the final product.[16]

More recent generative design cases includes Foster and Partners' Queen Elizabeth II Great Court, where the tessellated glass roof was designed using a geometric schema to define hierarchical relationships, and then the generated solution was optimized based on geometrical and structural requirement.[17]

Component design

NASA has begun using generative design in its parts. What they call "evolved structures" minimize weight and stress concentrations common in human designs. Weights go down by as much as two thirds, while stress factors are nearly 10 times lower. Such parts are often made via additive manufacturing. Design and manufacturing can take as little as one week. Parts appear in projects such as the Mars Sample Return mission, space telescopes, weather monitors, planetary instruments, and balloon observatories.[5]

See also


  1. ^ Meintjes, Keith. ""Generative Design" – What's That? - CIMdata". Retrieved 2018-06-15.
  2. ^ Caetano, Santos & Leitão 2020, p. 294.
  3. ^ Caetano, Santos & Leitão 2020, p. 295.
  4. ^ "Generative Design: The Road to Production". Retrieved 2019-12-05.
  5. ^ a b Blain, Loz (2023-02-15). "NASA's "evolved structures" radically reduce weight – and waiting". New Atlas. Retrieved 2023-02-16.
  6. ^ Schwab, Katharine (16 April 2019). "This is the first commercial chair made using generative design". Fast Company. Retrieved 13 August 2019.
  7. ^ Prasanta, Rajamoney, Shankar A. Rosenbloom, Paul S.; Wagner, Chris Bose (2014-09-04). Compositional model-based design: A generative approach to the conceptual design of physical systems. University of Southern California. OCLC 1003551283.((cite book)): CS1 maint: multiple names: authors list (link)
  8. ^ Barbieri, Loris; Muzzupappa, Maurizio (2022). "Performance-Driven Engineering Design Approaches Based on Generative Design and Topology Optimization Tools: A Comparative Study". Applied Sciences. 12 (4): 2106. doi:10.3390/app12042106.
  9. ^ Anderson, Fraser; Grossman, Tovi; Fitzmaurice, George (2017-10-20). Trigger-Action-Circuits: Leveraging Generative Design to Enable Novices to Design and Build Circuitry. ACM. pp. 331–342. doi:10.1145/3126594.3126637. ISBN 9781450349819. S2CID 10091635.
  10. ^ Mitchell, Melanie; Taylor, Charles E (1999). "Evolutionary computation: an overview". Annual Review of Ecology and Systematics. 30 (1): 593–616. doi:10.1146/annurev.ecolsys.30.1.593.
  11. ^ Shea, Kristina; Aish, Robert; Gourtovaia, Marina (2005). "Towards integrated performance-driven generative design tools". Automation in Construction. 14 (2): 253–264. doi:10.1016/j.autcon.2004.07.002.
  12. ^ Dapogny, Charles; Faure, Alexis; Michailidis, Georgios; Allaire, Grégoire; Couvelas, Agnes; Estevez, Rafael (2017). "Geometric constraints for shape and topology optimization in architectural design" (PDF). Computational Mechanics. 59 (6): 933–965. Bibcode:2017CompM..59..933D. doi:10.1007/s00466-017-1383-6. S2CID 41570887.
  13. ^ Krish, Sivam (2011). "A practical generative design method". Computer-Aided Design. 43 (1): 88–100. doi:10.1016/j.cad.2010.09.009.
  14. ^ Rittel, Horst W. J.; Webber, Melvin M. (1973). "Dilemmas in a General Theory of Planning" (PDF). Policy Sciences. 4 (2): 155–169. doi:10.1007/bf01405730. S2CID 18634229. Archived from the original (PDF) on 30 September 2007.
  15. ^ Hernandez, Carlos Roberto Barrios (2006). "Thinking parametric design: introducing parametric Gaudi". Design Studies. 27 (3): 309–324. doi:10.1016/j.destud.2005.11.006.
  16. ^ Edmondson, Amy C (2012). "Structure and pattern integrity". A Fuller explanation: The synergetic geometry of R. Buckminster Fuller (PDF). Springer Science & Business Media. pp. 54–60. doi:10.1007/978-1-4684-7485-5. ISBN 978-0-8176-3338-7.
  17. ^ Williams, Chris JK (2001). Burry, Mark; Datta, Sambit; Dawson, Anthony; Rollo, John (eds.). The analytic and numerical definition of the geometry of the British Museum Great Court Roof (PDF). Proceedings of mathematics & design 2001: the third international conference. Vol. 200. Geelong Vic Australia: Deakin University. pp. 434–440. ISBN 0-7300-2526-8.

Further reading