Skip to main content

Interior Illumination Design Using Genetic Programming

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9027)

Abstract

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including a many-objective problem involving 6 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Keywords

  • Illumination
  • Genetic programming
  • Radiance
  • Many-objective optimization

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-16498-4_14
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-16498-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    See http://www.cosc.brocku.ca/~bross/IllumGP/ for more details about this research.

  2. 2.

    Two-tailed unpaired t-test with unequal variance, \(p=0.05\) %.

References

  1. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  2. Larson, G.W., Shakespeare, R., Ehrlich, C., Mardaljevic, J., Phillips, E., Apian-Bennewitz, P.: Rendering with Radiance: The Art and Science of Lighting Visualization. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  3. Fuller, D., McNeil, A.: Radiance, http://www.radiance-online.org/

  4. Sims, K.: Interactive evolution of equations for procedural models. Vis. Comput. 9, 466–476 (1993)

    CrossRef  Google Scholar 

  5. Graf, J., Banzhaf, W.: Interactive Evolution of Images. In: Proceedings of International Conference on Evolutionary Programming, pp. 53–65 (1995)

    Google Scholar 

  6. Russell, S.: The Architecture of Light: Architectural Lighting Design Concepts and Techniques. Conceptnine, San Diego (2008)

    Google Scholar 

  7. Tena, J.E., Rudomin, I., Eugenio, A., Sada, G., Gordo, C.: An interactive system for solving inverse illumination problems using genetic algorithms. In: Proceedings of Computación Visual (1997)

    Google Scholar 

  8. Fernández, E., Besuievsky, G.: Inverse lighting design for interior buildings integrating natural and artificial sources. Comput. Graph. 36, 1096–1108 (2012)

    CrossRef  Google Scholar 

  9. Castro, F., del Acebo, E., Sbert, M.: Energy-saving light positioning using heuristic search. Eng. Appl. Artif. Intell. 25(3), 566–582 (2012)

    CrossRef  Google Scholar 

  10. Caldas, L.: Generation of energy-efficient architecture solutions applying gene\_arch: an evolution-based generative design system. Adv. Eng. Inform. 22(1), 59–70 (2008)

    CrossRef  MathSciNet  Google Scholar 

  11. Watanabe, M.S.: Induction Design: A Method for Evolutionary Design. Birkhauser, Basel (2002)

    Google Scholar 

  12. Marin, P., Bignon, J.C., Lequay, H.: Generative exploration of architectural envelope responding to solar passive qualities. In: Design & Decision Support Systems in Architecture and Urban Planning. Eindhoven U of Tech (2008)

    Google Scholar 

  13. Montana, D.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)

    CrossRef  Google Scholar 

  14. McKay, R., Hoai, N., Whigham, P., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. GPEM 11, 365–396 (2010)

    Google Scholar 

  15. Corne, D., Knowles, J.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of GECCO 2007, pp. 773–780. ACM Press (2007)

    Google Scholar 

  16. Flack, R.: Robgp - robust object based genetic programming system, September 2009. http://sourceforge.net/projects/robgp/

  17. Wiens, A., Ross, B.: Gentropy: evolutionary 2D texture generation. Comput. Graph. J. 26(1), 75–88 (2002)

    CrossRef  Google Scholar 

  18. Ebert, D., Musgrave, F., Peachey, D., Perlin, K., Worley, S.: Texturing and Modeling: A Procedural Approach, 2nd edn. Academic Press, New York (1998)

    Google Scholar 

  19. den Heijer, E., Eiben, A.: Comparing aesthetic measures for evolutionary art. Applications of Evolutionary Computation. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brian J. Ross .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Moylan, K., Ross, B. (2015). Interior Illumination Design Using Genetic Programming. In: Johnson, C., Carballal, A., Correia, J. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2015. Lecture Notes in Computer Science(), vol 9027. Springer, Cham. https://doi.org/10.1007/978-3-319-16498-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16498-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16497-7

  • Online ISBN: 978-3-319-16498-4

  • eBook Packages: Computer ScienceComputer Science (R0)