Generative Representations for Artificial Architecture and Passive Solar Performance TR

Aus de_evolutionary_art_org
Version vom 14. Januar 2015, 11:21 Uhr von Gbachelier (Diskussion | Beiträge)

(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu: Navigation, Suche


A. Harrington and Brian J. Ross: Generative Representations for Artificial Architecture and Passive Solar Performance. Brock COSC TR CS-13-02, March 2013.



This paper explores how the use of generative repre- sentations improves the quality of solutions in evolutionary design problems. A genetic programming system is developed with individuals encoded as generative representations. Two research goals motivate this work. One goal is to examine Hornby’s features and measures of modularity, reuse and hierarchy in new and more complex evolutionary design problems. In particular, we consider a more difficult problem domain where the generated 3D models are no longer constrained by voxels. Experiments are carried out to generate 3D models which grow towards a set of target points. The results show that the generative representations with the three features of modularity, regularity and hierarchy performed best overall. Although the measures of these features were largely consistent to those of Hornby, a few differences were found.

Our second research goal is to use the best performing encoding on some 3D modeling problems that involve passive solar performance criteria. Here, the system is challenged with generating forms that optimize exposure to the Sun. This is complicated by the fact that a model’s structure can interfere with solar exposure to itself; for example, protrusions can block Sun exposure to other model elements. Furthermore, external environmental factors (geographic location, time of the day, time of the year, other buildings in the proximity) may also be relevant. Experimental results were successful, and the system was shown to scale well to the architectural problems studied.

Extended Abstract


Used References

[1] K. Terzidis, Algorithmic Architecture. Architectural Press, 2006, vol. 1.

[2] P. von Buelow, Genetically Engineered Architecture - Design Explo- ration with Evolutionary Computation. Saarbr ̈ucken, Germany: VDM Verlag, 2007.

[3] M. Watanabe, Induction Design: A Method for Evolutionary Design. Birkh ̈auser, 2002.

[4] G. Stiny, “Introduction to shape and shape grammars,” Environment and Planning B: Planning and Design, vol. 7, no. 3, pp. 343–351, May 1980.

[5] J. Halatsch, A. Kunze, and G. Schmitt, “Using shape grammars for master planning,” Proc. Design Computing and Cognition 08, pp. 655– 673, 2008.

[6] Y. I. H. Parish and P. M ̈uller, “Procedural modeling of cities,” in ProceedingsSIGGRAPH ’01. New York, NY: ACM, 2001, pp. 301–308.

[7] P. M ̈uller, P. Wonka, S. Haegler, A. Ulmer, and L. Van Gool, “Procedural modeling of buildings,” ACM Trans. Graph., vol. 25, no. 3, pp. 614–623, Jul. 2006.

[8] E. Whiting, J. Ochsendorf, and F. Durand, “Procedural modeling of structurally-sound masonry buildings,” ACM Trans. Graph., vol. 28, no. 5, pp. 112:1–112:9, Dec. 2009.

[9] M. Lipp, P. Wonka, and M. Wimmer, “Interactive visual editing of gram- mars for procedural architecture,” in ACM SIGGRAPH 2008 papers, 2008, pp. 102:1–102:10.

[10] D. W. Corne and P. J. Bentley, Creative Evolutionary Systems (The Morgan Kaufmann Series in Artificial Intelligence), 1st ed. Morgan Kaufmann, Jul. 2001.

[11] U.-M. O’Reilly and M. Hemberg, “Integrating generative growth and evolutionary computation for form exploration,” Genetic Programming and Evolvable Machines, vol. 8, no. 2, pp. 163–186, June 2007, special issue on developmental systems.

[12] M. Hemberg, U.-M. O’Reilly, A. Menges, K. Jonas, M. Gonc ̧alves, and S. Fuchs, “Genr8: Architects’ experience with an emergent design tool,” in The Art of Artificial Evolution, 2008, pp. 167–188.

[13] J. Frazer, An evolutionary architecture. Architectural Association, 1995.

[14] J. Frazer, J. Frazer, L. Xiyu, T. Mingxi, and P. Janssen, “Generative and Evolutionary Techniques for Building Envelope Design,” 2002.

[15] M. ONeill, J. McDermott, J. M. Swafford, J. Byrne, E. Hemberg, A. Brabazon, E. Shotton, C. McNally, and M. Hemberg, “Evolutionary design using grammatical evolution and shape grammars: Designing a shelter,” International Journal of Design Engineering, vol. 3, no. 1, pp. 4–24, 2010.

[16] J. McDermott, J. M. Swafford, M. Hemberg, J. Byrne, E. Hemberg, M. Fenton, C. McNally, E. Shotton, and M. ONeill, “String-rewriting grammars for evolutionary architectural design,” Environment and Planning B: Planning and Design, vol. 39, no. 4, pp. 713–731, 2012. [Online]. Available:

C. Coia and B. J. Ross, “Automatic evolution of conceptual building architectures,” in IEEE Congress on Evolutionary Computation, New Orleans, LA, USA, 2011, pp. 1140–1147.

R. Kicinger, T. Arciszewski, and K. D. Jong, “Evolutionary computation and structural design: A survey of the state-of-the-art,” Comput. Struct., vol. 83, no. 23-24, pp. 1943–1978, Sep. 2005.

G. S. Hornby, “Generative representations for evolutionary design au- tomation,” Ph.D. dissertation, Brandeis University, USA, 2003.

A. Lindenmayer, “Mathematical models for cellular interaction in devel- opment: Parts i and ii.” Journal of Theoretical Biology, vol. 18, 1968.

S. Bergen, “Automatic structure generation using genetic programming and fractal geometry,” Master’s thesis, Brock University, 2011.

C. Jacob, Illustrating Evolutionary Computation with Mathematica. Morgan Kaufmann, 2001.

G. S. Hornby, “Functional Scalability through Generative Representa- tions: the Evolution of Table Designs,” Environment and Planning B: Planning and Design, vol. 31, no. 4, pp. 569–587, Jul. 2004.

——, “Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design,” in GECCO ’05. New York, NY, USA: ACM, 2005, pp. 1729–1736.

B. Anderson and M. Wells, Passive solar energy: the homeowner’s guide to natural heating and cooling. Brick House Pub. Co., 1981.

A. Harrington, “Enabling and measuring complexity in evolving designs using generative representations for artificial architecture,” Master’s thesis, Brock University, 2012.

J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press, 1992.

S. Luke, “ECJ - a java-based evolutionary computation research system,” in

A. Glassner, An Introduction to Ray Tracing. Academic Press, 1989.

C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolu- tionary Computation). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.

P. J. Bentley and J. P. Wakefield, “Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms,” in Soft Computing in Engineering Design and Manufacturing, P. K. Chawdhry, R. Roy, and R. K. Pant, Eds. Springer-Verlag, Jan. 1998, pp. 231–240.

D. Corne and J. Knowles, “Techniques for highly multiobjective optimi- sation: Some nondominated points are better than others,” in Proceedings GECCO 2007. ACM Press, 2007, pp. 773–780.

R. W. J. Flack and B. J. Ross, “Evolution of architectural floor plans,” in EvoApplications (2), 2011, pp. 313–322.


Full Text

intern file

Sonstige Links