This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto. Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step, we propose to employ a machine learning process: a neural net trained to perform image classification. This modified process is different enough from traditional methods as to warrant an adjustment of the terms of GAD. Through the development of this framework, we seek to demonstrate that generative evaluation may be seen as a new locus of subjectivity in design.
KeywordsMachine learning Generative design Design methods
- DeLanda, M.: Deleuze and the use of the genetic algorithm in architecture. Archit. Des. 71(7), 9–12 (2002)Google Scholar
- DeLanda, M.: The use of genetic algorithms in art. In: Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA), pp. 25–31. San Francisco (2012). http://cumincad.scix.net/cgi-bin/works/Show?acadia12_25
- Evans, R.: Translations from Drawing to Building. MIT Press, Cambridge (1997)Google Scholar
- Peng, W., Zhang, F., Nagakura, T.: Machines’ perception of space. In: Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA), Cambridge, MA. Association for Computer Aided Design in Architecture (2017)Google Scholar
- Rutten, D.: Galapagos Evolutionary Solver. http://www.grasshopper3d.com/groups/group/show?groupUrl=galapagos. Accessed 16 May 2018
- Wortmann, T.: Opossum - introducing and evaluating a model-based optimization tool for grasshopper. In: Janssen, P., Loh, P., Raonic, A., Schnabel, M.A. (Eds.) Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi’an Jiaotong-Liverpool University, Suzhou, China, 5–8 April 2017, pp. 283–292. CUMINCAD (2017)Google Scholar