Evolution of Architectural Floor Plans

  • Robert W. J. Flack
  • Brian J. Ross
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)


Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. This paper describes new techniques for automating the layout planning process using evolutionary computation. New innovations include allowing polygonal exteriors and multiple floors. Multi-objective ranking algorithms are tested to balance the many objectives in this problem. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth of problems to be considered. A variety of pleasing plans have been evolved with the approach.


evolutionary design floor planning genetic algorithms multi-objective optimization Pareto ranking ranked sum 


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  1. 1.
    Bentley, P.J., Corne, D.W. (eds.): Creative Evolutionary Systems. Morgan Kaufmann Publishers Inc., San Francisco (2002)Google Scholar
  2. 2.
    Bidarra, R., Tutenel, T., Smelik, M.R.: Rule-based layout solving and its application to procedural interior generation. In: Proceedings of CASA Workshop on 3D Advanced Media In Gaming And Simulation (3AMIGAS) (2009)Google Scholar
  3. 3.
    Bruls, M., Huizing, K., van Wijk, J.: Squarified treemaps. In: Proc. TCVG 2000, pp. 33–42. IEEE Press, Los Alamitos (2000)Google Scholar
  4. 4.
    Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proc. GECCO 2007, pp. 773–780. ACM, New York (2007)Google Scholar
  5. 5.
    Doulgerakis, A.: Genetic Programming + Unfolding Embryology in Automated Layout Planning. Master’s thesis, University Of London (2007)Google Scholar
  6. 6.
    Flack, R.W.J.: Evolution of Architectural Floor Plans. Master’s thesis, Brock University, St Catharines, Ontario, Canada (2011)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Hahn, E., Bose, P., Whitehead, A.: Persistent realtime building interior generation. In: Proc. Sandbox 2006, pp. 179–186. ACM, New York (2006)Google Scholar
  9. 9.
    Hirsch Jr., W.J.: Designing Your Perfect House. Dalsimer Press (2008)Google Scholar
  10. 10.
    Marson, F., Musse, S.R.: Automatic real-time generation of floor plans based on squarified treemaps algorithm. International Journal of Computer Games Technology (2010)Google Scholar
  11. 11.
    Martin, J.: Algorithmic beauty of buildings: Methods for procedural building generation. Tech. rep., Trinity University, San Antonio, TX, USA (2004)Google Scholar
  12. 12.
    Mitchell, W.J., Steadman, J.P., Liggett, R.S.: Synthesis and optimization of small rectangular floor plans. Environment and Planning B: Planning and Design 3(1), 37–70 (1976)CrossRefGoogle Scholar
  13. 13.
    Schnier, T., Gero, J.S.: Learning genetic representations as alternative to hand-coded shape grammars. In: Proc. AI in Design 1996, pp. 39–57. Kluwer, Dordrecht (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robert W. J. Flack
    • 1
  • Brian J. Ross
    • 1
  1. 1.Dept of Computer ScienceBrock UniversitySt CatharinesCanada

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