Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-28T05:03:54.694Z Has data issue: false hasContentIssue false
  • English
  • Français

Hierarchical structuring of layout problems in an interactive evolutionary layout system

Published online by Cambridge University Press:  20 April 2012

Reinhard Koenig  and
Sven Schneider
Reinhard Koenig*
Affiliation:
Department of Computer Science in Architecture, Bauhaus-University Weimar, Weimar, Germany
Sven Schneider
Affiliation:
Department of Computer Science in Architecture, Bauhaus-University Weimar, Weimar, Germany
*
Reprint requests to: Reinhard Koenig, Department of Computer Science in Architecture, Bauhaus-University Weimar, Belvederer Allee 1, Weimar 99423, Germany. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper focuses on computer-based generative methods for layout problems in architecture and urban planning with special regard for the hierarchical structuring of layout elements. The generation of layouts takes place using evolutionary algorithms, which are used to optimize the arrangement of elements in terms of overlapping within a given boundary and the topological relations between them. In this paper, the approach is extended by a data structure that facilitates the hierarchical organization of layout elements making it possible to structure and organize larger layout problems into subsets that can be solved in parallel. An important aspect for the applicability of such a system in the design process is an appropriate means of user interaction. This depends largely on the calculation speed of the system and the variety of viable solutions. These properties are evaluated for hierarchical as well as for nonhierarchical structured layout problems.

Keywords

Automated Layout DesignEvolutionary AlgorithmsHierarchical Structuring
Type
Special Issue Articles
Information
AI EDAM , Volume 26 , Issue 2: Design Computing and Cognition (DCC'10) , May 2012 , pp. 129 - 142
Copyright
Copyright © Cambridge University Press 2012

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

REFERENCES

Arvin, S.A., & House, D.H. (2002). Modeling architectural design objectives in physically based space planning. Automation in Construction 11, 213–225.CrossRefGoogle Scholar
Bäck, T., Hoffmeister, F., & Schwefel, H.-P. (1991). A survey of evolution strategies. Proc. 4th int. Conf. Genetic Algorithms.Google Scholar
Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning and Design 21(7), 31–48.Google Scholar
Coates, P., Healy, N., Lamb, C., & Voon, W.L. (1996). The use of cellular automata to explore bottom up architectonic rules. In Eurographics '96 (Rossignac, J., & Sillion, F., Eds.). Poitiers: Blackwell.Google Scholar
Coates, P., & Schmid, C. (2000). Agent based modelling. London: University of East London School of Architecture, Centre for Computing & Environment in Architecture. Accessed at http://uelceca.net/research/ECAADE/agentnotes%20the%20paper398liverpool%201999.pdfGoogle Scholar
Coello, C.A., Lamont, G.B., & Veldhuizen, D.A.V. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd ed.). Berlin: Springer.Google Scholar
Corne, D.W., Knowles, J.D., & Martin, J.O. (2000). The Pareto envelope-based selection algorithm for multiobjective optimization. Proc. Nature VI Conference Parallel Problem Solving.Google 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
Duarte, J.P. (2000). Customizing Mass Housing: A Discursive Grammar for Siza's Malagueira Houses. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
Elezkurtaj, T. (2004). Evolutionäre Algorithmen zur Unterstützung des kreativen architektonischen Entwerfens. Wien: TU-Wien.Google Scholar
Elezkurtaj, T., & Franck, G. (2001). Evolutionary algorithm in urban planning. Proc. CORP 2001, Information Technology in Urban and Spatial Planning, Vienna.Google Scholar
Elezkurtaj, T., & Franck, G. (2002). Algorithmic support of creative architectural design. Umbau 19, 129–137.Google Scholar
Ernst, G., & Newell, A. (1969). GPS: A Case Study in Generality and Problem Solving. New York: Academic Press.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 (Gero, J.S., Ed.), pp. 817–838. Boston: Kluwer Academic.Google Scholar
Getzels, J.W., & Csikszentmihalyi, M. (1967). Scientific creativity. Science Journal 3, 80–84.Google Scholar
Goldberg, D.E., & Lingle, R. (1985). Alleles, loci and the traveling salesman problem. Proc. int. Conf. Genetic Algorithms.Google 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.Google Scholar
Hower, W. (1997). Placing computations by adaptive procedures. Artificial Intelligence in Engineering 11, 307–317.Google Scholar
Jo, J.H., & Gero, J.S. (1998). Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering 12, 149–162.Google Scholar
Kursawe, F. (1990). A variant of evolution strategies for vector optimization. In Parallel Problem Solving from Nature, Vol. 1, pp. 193–197. Berlin: Springer.Google Scholar
Lawson, B. (2006). How Designers Think: The Design Process Demystified (4th ed.). Oxford: Architectural Press.CrossRefGoogle Scholar
Li, S.-P., Frazer, J.H., & Tang, M.-X. (2000). A constraint based generative system for floor layouts. Proc. 5th Conf. Computer Aided Architectural Design Research in Asia (CAADRIA), Singapore.Google Scholar
March, L., & Steadman, P. (1971). The Geometry of Environment: An Introduction to Spatial Organization in Design. London: R.I.B.A. Press.Google Scholar
Medjdoub, B., & Yannou, B. (2001). Dynamic space ordering at a topological level in space planning. Artificial Intelligence in Engineering 15, 47–60.Google Scholar
Michalek, J., & Papalambros, P. (2002). Interactive design optimisation of architectural layouts. Engineering Optimization 34, 485–501.Google Scholar
Miller, G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97.Google Scholar
Rechenberg, I. (1994). Evolutionsstrategie '94. Stuttgart: Frommann Holzboog.Google Scholar
Röpke, J. (1977). Die Strategie der Innovation: Eine systemtheoretische Untersuchung der Interaktion von Individuum Organisation und Markt im Neuerungsprozess. Tübingen: Mohr Siebeck.Google Scholar
Rosenman, M.A. (1997). The generation of form using an evolutionary approach. In Evolutionary Algorithms in Engineering Applications (Dasgupta, D., & Michalewicz, Z., Eds.). New York: Springer.Google Scholar
Ruch, J. (1978). Interactive space layout: a graph theoretical approach. Proc. 15th Design Automation Conf.CrossRefGoogle Scholar
Schaffer, J.D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proc. 1st Int. Conf. Genetic Algorithms.Google Scholar
Schneider, S., Fischer, J.-R., & König, R. (2010). Rethinking automated layout design—developing a creative evolutionary design method for the layout problems in architecture and urban design. Proc. Design Computing and Cognition DCC10, Stuttgart, Germany.Google Scholar
Schneider, S., Koenig, R., & Pohle, R. (2011). Who cares about right angles? Overcoming barriers in creating rectangularity in layout structures. Proc. eCAADe 2011: Respecting Fragile Places, Ljubljana, Slovenia.Google Scholar
Simon, H.A. (1969). The Sciences of the Artificial (1st ed.). Cambridge, MA: MIT Press.Google Scholar
Stiny, G., & Mitchell, W.J. (1978). The Palladian grammar. Environment and Planning B: Planning and Design 5(1), 5–18.Google Scholar
Weinzapfel, G., Johnson, T.E., & Perkins, J. (1971). IMAGE: an interactive computer system for multi-constrained spatial synthesis. Proc. 8th Design Automation Workshop.Google Scholar
Whitehead, B., & Eldars, M.Z. (1964). An approach to the optimum layout of single-storey buildings. Architects Journal 1373–1380.Google Scholar