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Comparing two evolutionary algorithm based methods for layout generation: Dense packing versus subdivision

Published online by Cambridge University Press:  22 July 2014

Reinhard Koenig  and
Katja Knecht
Reinhard Koenig*
Affiliation:
Faculty of Architecture, ETH Zurich, Zurich, Switzerland
Katja Knecht
Affiliation:
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
*
Reprint requests to: Reihard Koenig, Faculty of Architecture, ETH Zurich, Wolfgang-Pauli-Strasse 27, HIT H 31.6, Zurich 8092, Switzerland. E-mail: [email protected]
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Abstract

We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms. We analyze the characteristics of the two methods on the basis of three floor plan scenarios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally, we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.

Keywords

Dense PackingEvolutionary AlgorithmLayout GenerationSubdivision
Type
Research Article
Information
AI EDAM , Volume 28 , Issue 3: Computational Design Synthesis , August 2014 , pp. 285 - 299
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Arvin, S.A., & House, D.H. (2002). Modeling architectural design objectives in physically based space planning. Automation in Construction 11, 213–225.Google Scholar
Bäck, T. (1994). Evolutionary Algorithm in Theory and Practice. Oxford: Oxford University Press.Google Scholar
Bäck, T. (2000). Introduction to evolutionary algorithms. In Evolutionary Computation: 1. Basic Algorithms and Operators (Bäck, T., Fogel, D.B., & Michalewicz, T., Eds.), pp. 59–64. New York: Taylor & Francis.Google Scholar
Bäck, T., Hoffmeister, F., & Schwefel, H.-P. (1991). A survey of evolution strategies. Proc. 4th Int. Conf. Genetic Algorithms.Google Scholar
Cagan, J., Shimada, K., & Yin, S.S. (2002). A survey of computational approaches to threedimensional layout problems. Computer-Aided Design 34, 597–611. doi:10.1016/S0010-4485(01)00109-9Google Scholar
Coyne, R.F., & Flemming, U. (1990). Planning in design synthesis: abstraction-based LOOS. In Artificial Intelligence in Engineering V (Gero, J.S., Ed.), Vol. 1, pp. 91–111. New York: Springer.Google Scholar
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. New York: Wiley.Google Scholar
Elezkurtaj, T., & Franck, G. (2001). Evolutionary algorithm in urban planning. Proc. CORP 2001, Information Technology in Urban- and Spatial Planning Conf., Vienna.Google Scholar
Elezkurtaj, T., & Franck, G. (2002). Algorithmic support of creative architectural design. Umbau 19, 129–137. Accessed at http://www.iemar.tuwien.ac.at/publications/Google Scholar
Flack, R.W.J., & Ross, B.J. (2011). Evolution of architectural floor plans. Proc. 9th European Event on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMusArt 2011. Torino, Italy: Springer–Verlag.Google Scholar
Flemming, U. (1989). More on the representation and generation of loosely packed arrangements of rectangles. Environment and Planning B: Planning and Design 16(3), 327–359.CrossRefGoogle Scholar
Flemming, U., Baykan, C.A., Coyne, R.F., & Fox, M.S. (1992). Hierarchical generate-and-test vs. constraint-directed search: a comparison in the context of layout synthesis. In Artificial Intelligence in Design ’92 (Gero, J.S., Ed.), pp. 817–838. Boston: Kluwer Academic.CrossRefGoogle Scholar
Flemming, U., & Woodbury, R. (1995). Software environment to support early phases in building design (SEED): overview. Journal of Architectural Engineering 1, 147–152.CrossRefGoogle Scholar
Frew, R.S. (1980). A survey of space allocation algorithms in use in architectural design in the past twenty years. Proc. 17th Design Automation Conf., DAC ’80, New York.Google Scholar
Galle, P. (1981). An algorithm for exhaustive generation of building floor plans. Communications of the ACM 24, 813–825.CrossRefGoogle Scholar
Gero, J.S., & Kazakov, V.A. (1996). Learning and re-using information in space layout planning problems using genetic engineering. Artificial Intelligence in Engineering 11, 329–334.CrossRefGoogle Scholar
Grason, J. (1971). An approach to computerized space planning using graph theory. Proc. Design Automation Workshop, Atlantic City, NJ.Google Scholar
Hahn, E., Bose, P., & Whitehead, A. (2006). Persistent realtime building interior generation. Sandbox Symposium 2006, Boston.CrossRefGoogle Scholar
Harada, M., Witkin, A., & Baraff, D. (1995). Interactive physically-based manipulation of discrete/continuous models. Proc. 22nd Annual Conf. Computer Graphics and Interactive Techniques. New York: ACM.Google Scholar
Homayouni, H. (2000). A survey of computational approaches to space layout planning (1965–2000), pp. 1–18. Seattle, WA: University of Washington, Department of Architecture and Urban Planning.Google Scholar
Homayouni, H. (2006). A literature review of computational approaches to space layout planning, pp. 1–27. Seattle, WA: University of Washington, Department of Architecture and Urban Planning.Google Scholar
Hower, W., & Graf, W.H. (1996). A bibliographical survey of constraint-based approches to CAD, graphics, layout, visualization, and related topics. Knowledge-Based Systems 9, 449–464.Google Scholar
Jo, J.H., & Gero, J.S. (1998). Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering 12(3), 149–162.Google Scholar
Kalay, Y.E. (2004). Architecture's New Media: Principles, Theories, and Methods of Computer-Aided Design. Cambridge, MA: MIT Press.Google Scholar
Knecht, K., & Koenig, R. (2012). Layouts mittels Unterteilungsalgorithmen. In Kremlas: Entwicklung einer kreativen evolutionären Entwurfsmethode für Layoutprobleme in Architektur und Städtebau (Donath, D., Koenig, R., & Petzold, F., Eds.), pp. 113–129. Weimar, Germany: Bauhaus-Universität Weimar.Google Scholar
Koenig, R., & Schneider, S. (2012). Hierarchical structuring of layout problems in an interactive evolutionary layout system. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(2), 129–142.Google Scholar
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
Krishnamurti, R., & Earl, C.F. (1998). Densely packed rectangulations. Environment and Planning B: Planning and Design 25, 773–787. doi:10.1068/b250773Google Scholar
Kursawe, F. (1990). A variant of evolution strategies for vector optimization. Parallel Problem Solving From Nature I (Schwefel, H.-P., & Männer, R., Eds.), LNCS Vol. 496, pp. 193–197. Berlin: Springer.Google Scholar
March, L., & Steadman, P. (1974). The Geometry of Environment: An Introduction to Spatial Organization in Design, 2nd ed.Cambridge, MA: MIT Press.Google Scholar
Marson, F., & Musse, S.R. (2010). Automatic real-time generation of floor plans based on squarified treemaps algorithm. International Journal of Computer Games Technology. Advance online publication. doi:10.1155/2010/624817CrossRefGoogle Scholar
Michalek, J.J., & Papalambros, P.Y. (2002). Interactive design optimization of architectural layouts. Engineering Optimization 34(5), 485–501. doi:10.1080/03052150214016Google Scholar
Mitchell, W.J. (1998). The Logic of Architecture: Design, Computation, and Cognition, 6th ed.Cambridge, MA: MIT Press.Google Scholar
Mitchell, W.J., Steadman, P., & Liggett, R.S. (1976). Synthesis and optimization of small rectangular floor plans. Environment and Planning B: Planning and Design 3(1), 37–70.Google Scholar
Moore, A.W. (1991). An introductory tutorial on kd-trees. Report No. 209, Computer Laboratory, University of Cambridge.Google Scholar
Müller, P., Wonka, P., Haegler, S., Ulmer, A., & Van Gool, L. (2006). Procedural modeling of buildings. Proc. ACM SIGGRAPH 2006/ACM Transactions on Graphics Conf., Boston.Google Scholar
Müller, P., Zeng, G., Wonka, P., & Van Gool, L. (2007). Image-based procedural modeling of facades. Proc. ACM SIGGRAPH 2007/ACM Transactions on Graphics Conf., San Diego, CA.Google Scholar
Otten, R.H.J.M. (1982). Automatic floorplan design. Proc. 19th Design Automation Conf., Piscataway, NJ.Google Scholar
Parish, Y.I.H., & Müller, P. (2001). Procedural modeling of cities. Proc. SIGGRAPH, Los Angeles.Google Scholar
Rittel, H.W.J. (1992). Planen, Entwerfen, Design: Ausgewählte Schriften zu Theorie und Methodik. Stuttgart: Kohlhammer.Google Scholar
Rosenman, M.A., & Gero, J.S. (1999). Evolving designs by generating useful complex gene structures. In Evolutionary Design by Computers (Bentley, P.J., Ed.). San Francisco, CA: Morgan Kaufmann.Google Scholar
Schaffer, J.D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proc. 1st Int. Conf. Genetic Algorithms.Google Scholar
Schnier, T., & Gero, J.S. (1996). Learning genetic representations as alternative to hand-coded shape grammars. In Artificial Intelligence in Design ’96 (Gero, J.S., & Sudweeks, Eds.), pp. 39–57. Dordrecht: Kluwer.Google Scholar
Whitehead, B., & Eldars, M.Z. (1964). An approach to the optimum layout of single-story buildings. Architects' Journal 17, 1373–1379.Google Scholar