Skip to main content Accessibility help
×
Home
Hostname: page-component-5c569c448b-w5x57 Total loading time: 0.256 Render date: 2022-07-03T14:37:51.575Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Article contents

Dexen: A scalable and extensible platform for experimenting with population-based design exploration algorithms

Published online by Cambridge University Press:  07 October 2015

Patrick Janssen*
Affiliation:
Department of Architecture, National University of Singapore, Singapore
*
Reprint requests to: Patrick Janssen, Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117 566. E-mail: patrick@janssen.name

Abstract

A platform for experimenting with population-based design exploration algorithms is presented, called Dexen. The platform has been developed in order to address the needs of two distinct groups of users loosely labeled as researchers and designers. Whereas the researchers group focuses on creating and testing customized toolkits, the designers group focuses on applying these toolkits in the design process. A platform is required that is scalable and extensible: scalable to allow computationally demanding population-based exploration algorithms to be executed on distributed hardware within reasonable time frames, and extensible to allow researchers to easily implement their own customized toolkits consisting of specialized algorithms and user interfaces. In order to address these requirements, a three-tier client–server system architecture has been used that separates data storage, domain logic, and presentation. This separation allows customized toolkits to be created for Dexen without requiring any changes to the data or logic tiers. In the logic tier, Dexen uses a programming model in which tasks only communicate through data objects stored in a key-value database. The paper ends with a case study experiment that uses a multicriteria evolutionary algorithm toolkit to explore alternative configurations for the massing and façade design of a large residential development. The parametric models for developing and evaluating design variants are described in detail. A population of design variants are evolved, a number of which are selected for further analysis. The case study demonstrates how evolutionary exploration methods can be applied to a complex design scenario without requiring any scripting.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahuja, S., Carriero, N., & Gelernter, D. (1986). Linda and friends. Computer 19(8), 2634.CrossRefGoogle Scholar
Bentley, P.J. (1999). An introduction to evolutionary design by computers. In Evolutionary Design by Computers (Bentley, P.J., Ed.), pp. 173. San Francisco, CA: Morgan Kaufmann.Google Scholar
Caldas, L. (2008). Generation of energy-efficient architecture solutions applying GENE_ARCH: an evolution-based generative design system. Advanced Engineering Informatics 22(1), 5970.CrossRefGoogle Scholar
Carriero, N.J, Gelernter, D., Mattson, T.G., & Sherman, A.H. (1994). The Linda alternative to message-passing systems. Parallel Computing, Message Passing Interfaces 20(4), 633655.CrossRefGoogle Scholar
Chee, Z.J., & Janssen, P.H.T. (2013). Exploration of urban street patterns: multi-criteria evolutionary optimisation using axial line analysis. Proc. 18th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2013), pp. 695704, Singapore, May 15–17.Google Scholar
Chian, E., & Janssen, P.H.T. (2014). Exploring urban configurations for a walkable new town using evolutionary algorithms. Proc. 19th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2014), pp. 233242, Kyoto, Japan, May 14–17.Google Scholar
Coello, C. A., Lamont, G.B., & van Veldhuizen, D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed.New York: Springer.Google Scholar
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. New York: Wiley.Google Scholar
Darke, J. (1979). The primary generator and the design process. Design Studies 1(1), 3644.CrossRefGoogle Scholar
Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction. Hoboken, HJ: Wiley.CrossRefGoogle Scholar
Flager, F., Welle, B., Bansal, P., Soremekun, G., & Haymaker, J. (2009). Multidisciplinary process integration and design optimisation of a classroom building. Journal of Information Technology in Construction 14, 595612.Google Scholar
Fonseca, C.M., Paquete, L., & Ibáñez, M.L. (2006). An improved dimension-sweep algorithm for the hypervolume indicator. Proc. 2006 Congr. Evolutionary Computation (CEC'06), pp. 11571163, Faro, Portugal, July 16–26.CrossRefGoogle Scholar
Frazer, J.H. (1974). Reptiles. Architectural Design 4, 231239.Google Scholar
Frazer, J. H. (1995). An Evolutionary Architecture. London: AA Publications.Google Scholar
Frazer, J.H., & Connor, J. (1979). A conceptual seeding technique for architectural design. Int. Conf. Application of Computers in Architectural Design and Urban Planning, pp. 425434. Berlin: AMK.Google Scholar
Gerber, D.J., & Lin, S.-H.E., (2013). Designing in complexity: simulation, integration, and multidisciplinary design optimization for architecture. Simulation 90(8), 936959.CrossRefGoogle Scholar
Geyer, P. (2009). Component-oriented decomposition for multidisciplinary design optimization in building design. Advanced Engineering Informatics 23(1), 1231.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.Google Scholar
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.Google Scholar
Janssen, P.H.T. (2004). A Design Method and a Computational Architecture for Generating and Evolving Building Designs. PhD Thesis. Hong Kong Polytechnic University, School of Design.Google Scholar
Janssen, P.H.T. (2006). The role of preconception in design. Proc. Design Computing and Cognition ’06, pp. 365383. Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
Janssen, P.H.T. (2009). An evolutionary system for design exploration. Proc. CAAD Futures ’09, pp. 259272, Montreal, Canada, June 7–9.Google Scholar
Janssen, P.H.T. (2013). Evo-Devo in the sky. Proc. 31st eCAADe Conf., pp. 205214. Delft, The Netherlands, September 18–20.Google Scholar
Janssen, P.H.T. (2014). Visual dataflow modelling: some thoughts on complexity. Proc. 32nd eCAADe Conf., pp. 547556, Newcastle, UK, September 10–12.Google Scholar
Janssen, P.H.T., Basol, C., & Chen, K.W. (2011). Evolutionary developmental design for non-programmers. Proc. 29th eCAADe Conf., pp. 245252, Ljubljana, Slovenia, September 21–24.Google Scholar
Janssen, P.H.T., & Chen, K.W. (2011). Visual dataflow modelling: a comparison of three systems. Proc. CAAD Futures ’11, pp. 801816, Liege, Belgium, July 4–8.Google Scholar
Janssen, P.H.T., & Frazer, J.H. (2005). A framework for generating and evolving building designs. International Journal of Architectural Computing 3(4), 449470.CrossRefGoogle Scholar
Janssen, P.H.T., Frazer, J.H., & Tang, M.-X. (2000). Evolutionary design systems: a conceptual framework for the creation of generative processes. Proc. 5th Int. Conf. Design Decision Support Systems in Architecture and Urban Planning, pp. 190200, Nijkerk, The Netherlands.Google Scholar
Janssen, P.H.T., Frazer, J.H., & Tang, M.-X. (2002). Evolutionary design systems and generative processes. Applied Intelligence 16(2), 119128.CrossRefGoogle Scholar
Janssen, P.H.T., & Kaushik, V.S. (2012). Iterative refinement through simulation: exploring trade-offs between speed and accuracy. Proc. 30th eCAADe Conf., pp. 555563, Prague, Czech Republic, September 12–14.Google Scholar
Janssen, P.H.T., & Kaushik, V.S. (2013). Decision chain encoding: evolutionary design optimization with complex constraints. Proc. 2nd EvoMUSART Conf., pp. 157167, Vienna, Austria, April 3–5.Google Scholar
Janssen, P.H.T., & Kaushik, V.S. (2014). Evolving Lego: exploring the impact of alternative encodings on the performance of evolutionary algorithms. Proc. 19th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2014), pp. 523532, Kyoto, Japan, May 14–17, 2014.Google Scholar
Janssen, P.H.T., & Stouffs, R. (2015). Types of parametric modelling. Proc. 20th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2015), pp. 157166, Daegu, Republic of Korea, May 20–23.Google Scholar
Kaushik, V.S., & Janssen, P.H.T. (2012). Multi-criteria evolutionary optimisation of building envelopes during conceptual stages of design. Proc. 17th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2012), pp. 497–506, Chennai, India, April 25–28.Google Scholar
Kaushik, V.S., & Janssen, P.H.T. (2013). An evolutionary design process: adaptive-iterative explorations in computational embryogenesis. Proc. 18th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2013), pp. 137146, Singapore, May 15–17.Google Scholar
Lee, X.W. (2011). Using evolutionary algorithm as a design tool for the multi-criteria optimization of catenary structures. Masters Thesis. National University of Singapore, Department of Architecture.Google Scholar
Lin, S.-H.E. (2014). Designing-in performance: energy simulation feedback for early stage design decision making. PhD Thesis. University of Southern California.Google Scholar
Lin, S.-H.E., & Gerber, D.J. (2014). Designing-in performance: a framework for evolutionary energy performance feedback in early stage design. Automation in Construction 38, 5973.CrossRefGoogle Scholar
Makimoto, T., & Manners, D. (1997). Digital Nomad. New York: Wiley.Google Scholar
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed.Berlin: Springer.CrossRefGoogle Scholar
Mueller, V., Crawley, D.B., & Zhou, X. (2013). Prototype implementation of a loosely coupled design performance optimisation framework. Proc. 18th Int. Conf. Computer-Aided Architectural Design Research in Asia (CAADRIA 2013), pp. 675684, Singapore, May 15–17.Google Scholar
Mueller, V., & Strobbe, T. (2013). Cloud-based design analysis and optimization framework. Proc. 31st eCAADe Conf., Vol. 2, pp. 185194, Delft, The Netherlands, September 18–20.Google Scholar
Schön, D. (1983). The Reflective Practitioner: How Professionals Think in Action. London: Temple Smith.Google Scholar
Turrin, M., von Buelow, P., Kilian, A., & Stouffs, R. (2012). Performative skins for passive climatic comfort: a parametric design process. Automation in Construction 22, 3650.CrossRefGoogle Scholar
von Buelow, P. (2012). ParaGen: performative exploration of generative systems. Journal of the International Association for Shell and Spatial Structures 53(4), 271284.Google Scholar
Welle, B., Haymaker, J., & Rogers, Z. (2011). ThermalOpt: a methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments. Building Simulation 4(4), 293313.CrossRefGoogle Scholar
Zhong, H. (2013). An urban farm typology to mitigate desertification in Wuwei, China. Masters Thesis. National University of Singapore, Department of Architecture.Google Scholar
Zitzler, E., & Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms—a comparative case study. Conf. Parallel Problem Solving From Nature (PPSN V), LNCS Vol. 1498, pp. 292301. Berlin: Springer.Google Scholar
3
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Dexen: A scalable and extensible platform for experimenting with population-based design exploration algorithms
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Dexen: A scalable and extensible platform for experimenting with population-based design exploration algorithms
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Dexen: A scalable and extensible platform for experimenting with population-based design exploration algorithms
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *