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

  • Patrick Janssen (a1)

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.

Corresponding author
Reprint requests to: Patrick Janssen, Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117 566. E-mail:
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Ahuja, S., Carriero, N., & Gelernter, D. (1986). Linda and friends. Computer 19(8), 2634.
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.
Caldas, L. (2008). Generation of energy-efficient architecture solutions applying GENE_ARCH: an evolution-based generative design system. Advanced Engineering Informatics 22(1), 5970.
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.
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.
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.
Coello, C. A., Lamont, G.B., & van Veldhuizen, D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed.New York: Springer.
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. New York: Wiley.
Darke, J. (1979). The primary generator and the design process. Design Studies 1(1), 3644.
Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction. Hoboken, HJ: Wiley.
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.
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.
Frazer, J.H. (1974). Reptiles. Architectural Design 4, 231239.
Frazer, J. H. (1995). An Evolutionary Architecture. London: AA Publications.
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.
Gerber, D.J., & Lin, S.-H.E., (2013). Designing in complexity: simulation, integration, and multidisciplinary design optimization for architecture. Simulation 90(8), 936959.
Geyer, P. (2009). Component-oriented decomposition for multidisciplinary design optimization in building design. Advanced Engineering Informatics 23(1), 1231.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
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.
Janssen, P.H.T. (2006). The role of preconception in design. Proc. Design Computing and Cognition ’06, pp. 365383. Dordrecht, The Netherlands: Springer.
Janssen, P.H.T. (2009). An evolutionary system for design exploration. Proc. CAAD Futures ’09, pp. 259272, Montreal, Canada, June 7–9.
Janssen, P.H.T. (2013). Evo-Devo in the sky. Proc. 31st eCAADe Conf., pp. 205214. Delft, The Netherlands, September 18–20.
Janssen, P.H.T. (2014). Visual dataflow modelling: some thoughts on complexity. Proc. 32nd eCAADe Conf., pp. 547556, Newcastle, UK, September 10–12.
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.
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.
Janssen, P.H.T., & Frazer, J.H. (2005). A framework for generating and evolving building designs. International Journal of Architectural Computing 3(4), 449470.
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.
Janssen, P.H.T., Frazer, J.H., & Tang, M.-X. (2002). Evolutionary design systems and generative processes. Applied Intelligence 16(2), 119128.
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.
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.
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.
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.
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.
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.
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.
Lin, S.-H.E. (2014). Designing-in performance: energy simulation feedback for early stage design decision making. PhD Thesis. University of Southern California.
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.
Makimoto, T., & Manners, D. (1997). Digital Nomad. New York: Wiley.
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed.Berlin: Springer.
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.
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.
Schön, D. (1983). The Reflective Practitioner: How Professionals Think in Action. London: Temple Smith.
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.
von Buelow, P. (2012). ParaGen: performative exploration of generative systems. Journal of the International Association for Shell and Spatial Structures 53(4), 271284.
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.
Zhong, H. (2013). An urban farm typology to mitigate desertification in Wuwei, China. Masters Thesis. National University of Singapore, Department of Architecture.
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.
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