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A Computational Design System with Cognitive Features Based on Multi-objective Evolutionary Search with Fuzzy Information Processing

  • Michael S. Bittermann
Conference paper

Abstract

A system for architectural design is presented, which is based on combining a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The system possesses cognitive features, where cognition is defined as final decision-making based not exclusively on optimization outcomes, but also on some higher-order aspects, which do not play role in the pure optimization process. That is, the machine is able to distinguish among the equivalently valid solution alternatives it generated, where the distinction is based on second order preferences that were not pin-pointed by the designer prior to the computational design process. This is accomplished through integrating fuzzy information processing into the multi-objective evolutionary search, so that second-order information can be inductively obtained from the search process. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.

Keywords

Pareto Front Multiobjective Optimization Pareto Optimal Solution Cognitive Feature Priority Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Eastman, C.M.: Automated space planning. Artificial Intelligence 4, 41–64 (1973)CrossRefGoogle Scholar
  2. 2.
    Flemming, U., Woodbury, R.: Software environment to support early phases in building design (SEED): Overview. J. of Architectural Engineering 1, 147–152 (1995)CrossRefGoogle Scholar
  3. 3.
    Koile, K.: An intelligent assistant for conceptual design. In: Gero, J.S. (ed.) Design Computing and Cognition 2004, pp. 3–22. Kluwer Academic Publishers, Massachusetts Institute of Technology, Boston, USA (2004)Google Scholar
  4. 4.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, Englewood Cliffs (1997)Google Scholar
  5. 5.
    Gero, J., Kazakov, V.: Evolving design genes in space layout problems. Artificial Intelligence in Engineering 12, 163–176 (1998)CrossRefGoogle Scholar
  6. 6.
    Damsky, J., Gero, J.: An evolutionary approach to generating constraint-based space layout topologies. In: Junge, R. (ed.) CAAD Futures 1997, pp. 855–874. Kluwer Academic Publishing, Dordrecht (1997)Google Scholar
  7. 7.
    Jo, J., Gero, J.: Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering 12, 163–176 (1998)CrossRefGoogle Scholar
  8. 8.
    Mawdesley, M.J., Al-jibouri, S.H., Yang, H.: Genetic Algorithms for Construction Site Layout in Project Planning. J. Constr. Engrg. and Mgmt. 128, 418–426 (2002)CrossRefGoogle Scholar
  9. 9.
    Caldas, L.: GENE_ARCH: An evolution-based generative design system for sustainable architecture. In: Smith, I.F.C. (ed.) EG-ICE 2006. LNCS (LNAI), vol. 4200, pp. 109–118. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Coello, C.A.C., Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multiobjective Problems. Kluwer Academic Publishers, Boston (2003)Google Scholar
  11. 11.
    Bittermann, M.S., Ciftcioglu, O.: A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm. In: IEEE Conference on Evolutionary Computation. IEEE, Trondheim (2009)Google Scholar
  12. 12.
    Zadeh, L.A.: Fuzzy logic, neural networks and soft computing. Communications of the ACM 37, 77–84 (1994)CrossRefGoogle Scholar
  13. 13.
    Ciftcioglu, O., Bittermann, M.S., Sariyildiz, I.S.: Building performance analysis supported by GA. In: Tan, K.C., Xu, J.X. (eds.) 2007 IEEE Congress on Evolutionary Computation, pp. 489–495. IEEE, Singapore (2007)Google Scholar
  14. 14.
    Deb, K.: Multiobjective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)Google Scholar
  15. 15.
    Tan, K.C., Xu, J.-X. (eds.): Proc. 2007 IEEE Congress on Evolutionary Computation. IEEE Cong., Singapore (2007)Google Scholar
  16. 16.
    Hughes, E.J.: Evolutionary many-objective optimisation: many once or one many? In: IEEE Congress on Evolutionary Computation CEC 2005, pp. 222–227. IEEE Service Center, Edinburgh (2005)CrossRefGoogle Scholar
  17. 17.
    Horn, J., Nafploitis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Michaelwicz, Z. (ed.) First IEEE Conf. on Evolutionary Computation, pp. 82–87. IEEE Press, Los Alamitos (1994)Google Scholar
  18. 18.
    Ciftcioglu, Ö., Bittermann, M.S.: Adaptive formation of Pareto front in evolutionary multi-objective optimization. In: Santos, W.P.d. (ed.), Evolutionary Computation. In-Tech, Vienna, pp. 417–444 (2009)Google Scholar
  19. 19.
    Bittermann, M.S., Sariyildiz, I.S., Ciftcioglu, Ö.: Visual perception in design and robotics. Integrated Computer-Aided Engineering 14, 73–91 (2007)Google Scholar

Copyright information

© Springer Netherlands 2011

Authors and Affiliations

  • Michael S. Bittermann
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

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