The Application of Genetic Algorithms to Conceptual Design

  • M G Hudson
  • I C Parmee

Abstract

Design is not a simple hierarchical process where the designer is presented with a set of requirements and works steadily through a decomposition strategy moving from abstract concepts to the final concrete product. The design problem is ill-defined and changes as the designer explores it through solutions and partial solutions. Designers use a ‘solution focused’ strategy. Feasible solutions are posed to probe the ‘instability of the problem’ and the ‘limitations of the way the problem is framed’. It is common for architects, in professional practice, to first simplify the problem so they can generate a rough solution. This solution is then used to develop understanding of the problem which leads to a gradual refinement of that solution9. Neither is design simply a matter of iteration around an essentially hierarchical process. Information gained during the design process can prompt the designer to transfer the design effort to higher levels, or to a location remote in the hierarchy. Design is essentially a heterarchical, possibly chaotic, process. The heterarchical nature of design is even more apparent in team design and enshrined in the philosophy of concurrent design. Given the perceived nature of the design process, adaptive computing techniques with their property of emergent behaviour present an attractive paradigm for conceptual design. An overview of the evolutionary design capabilities is presented in Appendix A for those who are unfamiliar with the techniques.

Keywords

Migration Recombination Metaphor Lawson Summing 

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References

  1. 1.
    Antonisse H. J. (1991), “A Grammar Based Genetic Algorithm”, In Rawlins G. J. E. (Ed) “Foundations of Genetic Algorithms”, Morgan Kaufmann PublishersGoogle Scholar
  2. 2.
    Coyne R. D. (1988), “Logic Models of Design”, PitmanGoogle Scholar
  3. 3.
    French M. J. (1988), “Invention and Evolution: Design in Nature and Engineering”, Cambridge University PressGoogle Scholar
  4. 4.
    Gero J. S. (1990), “Design Prototypes: A Knowledge Representation Schema for Design”, AI MagazineGoogle Scholar
  5. 5.
    Gero J. S. (1994), “Evolutionary Learning of Novel Grammars for Design Improvement”, Artificial Intelligence for Engineering Design, Analysis and Manufacture, 8, 83–94, 1994Google Scholar
  6. 6.
    Goldberg D. E. (1991), “Genetic Algorithms as a Computational Theory of Conceptual Design”, In Rzevski G. & Adeyi R. A. (Eds) “Applications of Artificial Intelligence in Engineering”, Elsevier Science PublishingGoogle Scholar
  7. 7.
    Goldberg D. E., Deb K., Karguta H. & Harik G. (1993), “Rapid, accurate optimization of difficult problems using fast messy genetic algorithms”, Proc. of the Fifth International Conference on Genetic Algorithms, pp56–64, Morgan Kaufman PublishersGoogle Scholar
  8. 8.
    Grierson D. (1994), “Conceptual Design using Emergent Computing Techniques”, NATO Advanced Research Workshop, Nafplio, Greece, August 25–27, 1994Google Scholar
  9. 9.
    Lawson B. (1990), “How Designers Think: The Design Process Demystified” ButterworthGoogle Scholar
  10. 10.
    Parmee I. C. (1993), “The Concrete Arch Dam, an Evolutionary Model of the Design Process”, In Albrecht R. F., Reeves C. R. & Steele N. C. (Eds), “Artificial Neural Nets and Genetic Algorithms”, Proc. of the International Conference in Innsbruck, Austria, pp 544–551, 1993Google Scholar
  11. 11.
    Parmee I. C. (1994) “Adaptive Search Techniques for Decision Support during Preliminary Engineering Design”, Procs. Information Technologies to Support Engineering Decision Making, Institute of Civil Engineers, November 1994Google Scholar
  12. 12.
    Parmee I. C. (1995), “High Level Decision Support for Engineering Design using the Genetic Algorithm and Complimentary Techniques”, Procs. Applied Decision Technologies, Brunei Conf. Centre, London, April 3–5, 1995Google Scholar
  13. 13.
    Parmee l. C. (1995), “Reinforcing the Natural Clustering Characteristics of the GA”, Internal Report PEDC-04–95, April 1995Google Scholar
  14. 14.
    Parmee l. C. & Denham M. J. (1994), “The Integration of Adaptive Search Techniques with Current Engineering Design Practice”, Proc. Adaptive Computing in Engineering Design and Control, ppl-13, Plymouth, 1994Google Scholar
  15. 15.
    Pham D. T. and Yang Y. (1993), “A Genetic Algorithm Based Preliminary Design System”, Proc. Instn Mech. Engrs, 1993, 207, 127–133Google Scholar
  16. 16.
    Rechenburg I. (1964), “Cybernetic Solution Path of an Experimental Problem”, Royal Aircraft Establishment, Library Translation 1122, FarnboroughGoogle Scholar
  17. 17.
    Woodbury R. F. (1993), “A Genetic Approach to Creative Design”, In Gero J. S. & Maher M. L. (Eds), “Modeling Creativity and Knowledge-Based Creative Design”, Lawrence Erlbaum Associates IncGoogle Scholar
  18. BibliographyGoogle Scholar
  19. 1.
    Davis L. (Ed) (1991), “Handbook of Genetic Algorithms”, Van Nostrand ReinholdGoogle Scholar
  20. 2.
    Goldberg D. E. (1989), “Genetic Algorithms in Search, Optimisation and Machine Learning”, Addison WesleyGoogle Scholar
  21. 3.
    Holland J. (1975), “Adaptation in Natural and Artificial Systems”, The University of Michigan PressGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1996

Authors and Affiliations

  • M G Hudson
  • I C Parmee

There are no affiliations available

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