Skip to main content

Advertisement

Log in

Architectural space planning using evolutionary computing approaches: a review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aliakseyeu D, Martens JB, Rauterberg M (2006) A computer support tool for the early stages of architectural design. Interact Comput 18(4): 528–555

    Article  Google Scholar 

  • Arabacioglu BC (2010) Using fuzzy inference system for architectural space analysis. Appl Soft Comput 10(3): 926–937

    Article  Google Scholar 

  • Bandaru S, Deb K (2010) Automated discovery of vital design knowledge from pareto-optimal solutions: first results from engineering design. IEEE Congress on Evolutionary Computation—CEC2010, Barcelona, Spain.

  • Caldas L (2006) GENE_ARCH: an evolution-based generative design system for sustainable architecture. In: Smith IFC (eds) Lecture notes in artificial intelligence. Springer, Berlin, pp 109–118

    Google Scholar 

  • Caldas L (2008) Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system. Adv Eng Inform 22(1): 59–70

    Article  MathSciNet  Google Scholar 

  • Damski JC, Gero JS (1997) An evolutionary approach to generating constraint based space-layout topologies. In: Junge R (eds) CAAD Futures. Kluwer Academic Publishing, Dordrecht, pp 855–874

    Google Scholar 

  • Demirkan H (1998) Integration of reasoning systems in architectural modeling activities. Autom Constr 7(2–3): 229–236

    Article  Google Scholar 

  • Donath D, Lobos D (2009) Plausibility in early stages of architectural design: a new tool for high-rise residential buildings. Tsinghua Sci Technol 14(3): 327–332

    Article  Google Scholar 

  • Eiben AE, Smith JE (2007) Introduction to evolutionary computing (Natural Computing Series) , 2nd edn. Springer, Berlin

    Google Scholar 

  • Frew RS (1980) A survey of space allocation algorithms in use in architectural design in the past twenty years. Annual ACM IEEE design automation conference proceedings of the 17th design automation conference. Minneapolis, Minnesota, United States, pp 165–174, 1980 ISBN:0-89791-020-6

  • Gen M, Lin L (2009) Evolutionary techniques for utomation. Springer handbook of automation part C:487–502, doi:10.1007/978-3-540-78831-7_29

  • Gero JS, Kazakov VA (1997) Learning and re-using information in space layout planning problems using genetic engineering. Artif Intell Eng 11(3): 329–334

    Article  Google Scholar 

  • Gero JS, Kazakov VA (1998) Evolving design genes in space layout planning problems. Artif Intell Eng 12(3): 163–176

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Boston

    MATH  Google Scholar 

  • Holland JJ (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Homayoumi H (2006) A survey of Computational Approaches to space layout planning, retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.4372 on 13/08/10

  • Inoue M, Takagi H (2009) Architectural room planning support system using methods of generating spatial layout plans and evolutionary multi-objective optimization. Trans Jpn Soc Artif Intell 24(1): 25–33

    Article  Google Scholar 

  • Jo JH, Gero JS (1998) Space layout planning using an evolutionary approach. Artif Intell Eng 12(3): 149–162

    Article  Google Scholar 

  • Karlen M (2009) Space planning basics, 3rd edn. Wiley, London, p 240

    Google Scholar 

  • Liggett RS (2000) Automated facilities layout: past, present and future. Automat Constr 9: 197–215

    Article  Google Scholar 

  • Lomas KJ (2007) Architectural design of an advanced naturally ventilated building form. Energy Build 39(2): 166–181

    Article  Google Scholar 

  • Medjdoub B, Yannou B (2000) Separating topology and geometry in space planning. Comput-Aided Des 32: 39–61

    Article  Google Scholar 

  • Michalek JJ, Choudhary R, Papalambros PY (2002) Architectural layout design optimization. Eng Optim 34(5): 461–484

    Article  Google Scholar 

  • Mourshed M, Manthilake I, Wright J (2009) Automated space layout planning for environmental sustainiability. In: Proceedings of 3rd CIB conference on sustainable building and development SABE2009, retrieved from http://www.sasbe2009.com/papers.html on 14/09/10

  • Rosenman M (1997) The generation of form using an evolutionary approach. In: Dasgupta D (eds) Evolutionary algorithms in engineering applications. Springer, Berlin, pp 69–86

    Google Scholar 

  • Scheurer F (2007) Getting complexity organised: using self-organisation in architectural construction. Autom Constr 16(1): 78–85

    Article  Google Scholar 

  • Schnier T, Gero JS (1996) Learning genetic representations as alternative to hand-coded shape grammars. In: Gero JS, Sudweeks F (eds) Artificial intelligence in design. Kluwer, Dordrecht, pp 39–57

    Google Scholar 

  • Serag A, Ono S, Nakayama S (2008) Using interactive evolutionary computation to generate creative building designs. Artif Life Robot 131: 246–250. doi:10.1007/s10015-008-0588-3

    Article  Google Scholar 

  • Shaviv E, Yezioro A, Capeluto IG, Peleg UJ, Kalay YE (1996) Simulations and knowledge-based computer-aided architectural design (CAAD) systems for passive and low energy architecture. Energy Build 23(3): 257–269

    Article  Google Scholar 

  • Tang X, Thomas S, Coleman P, Amato NM (2010) Reachable distance space: efficient sampling-based planning for spatially constrained systems. Int J Robot Res 29(7): 916–934

    Article  Google Scholar 

  • Verma M, Thakur MK (2010) Architectural space planning using genetic algorithms. The 2nd international conference on computer and automation engineering (ICCAE), Vol. 2 digital object identifier: doi:10.1109/ICCAE.2010.5451497, pp 268–275

  • Virirakis L (2003) GENETICA: a computer language that supports general formal expression with evolving data structures. IEEE Trans Evolut Comput 7(5): 456–481

    Article  Google Scholar 

  • Wong SSY, Chan KCC (2009) EvoArch: an evolutionary algorithm for architectural layout design. Comput-Aided design 41(9): 649–667

    Article  MathSciNet  Google Scholar 

  • Yeh IC (2006) Architectural layout optimization using annealed neural network. Autom Constr 15(4): 531–539

    Article  Google Scholar 

  • http://www.vaastu-shastra.com

  • http://en.wikipedia.org/wiki/Evolutionary_computation

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kamlesh Dutta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dutta, K., Sarthak, S. Architectural space planning using evolutionary computing approaches: a review. Artif Intell Rev 36, 311–321 (2011). https://doi.org/10.1007/s10462-011-9217-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-011-9217-y

Keywords

Navigation