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Genetic Programming within Civil Engineering

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Adaptive Computing in Design and Manufacture VI

Abstract

Genetic programming is a relatively new method of evolutionary computing with few published applications in civil engineering. This paper both describes and demonstrates how GP can be applied to structural optimisation and design problems to produce results that offer significant improvements over traditional GA based methods. The paper concludes by presenting the direction of the work currently being undertaken at Cardiff University, which includes the conceptual design of frame structures and the visualisation of results using VRML/X3D to produce virtual reality simulations that can be used as a collaborative design environment over the Internet.

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References

  1. Koza J.R., Genetic Programming: On the programming of computers by means of natural selection, Cambridge MA: MIT Press, ISBN 0-262-11170-5, 1992.

    MATH  Google Scholar 

  2. Holland J.H, Adaptation in natural and artificial systems, Ann Arbor: The University of Michigan Press, 1975.

    Google Scholar 

  3. Banzhaf W et al, Genetic Programming-An introduction (On the automatic evolution of computer programs and its applications), Morgan Kaufmann Publishers, ISBN 1-55860-510-X, 1998.

    Google Scholar 

  4. Montana D.J, “Strongly typed genetic programming”, Evolutionary computation, 3(2), 1995, pp199–230.

    Article  Google Scholar 

  5. Radcliffe N.J and Surry P.D, “Formal memetic algorithms”, Lecutre Notes in Computer Science 865, 199

    Google Scholar 

  6. Ashour A.F et al, “Empirical modelling of shear strength of RC deep beams by genetic programming”, Computers and Structures, Pergamon, 81 (2003), pp 331–338.

    Article  Google Scholar 

  7. Hong YS and Bhamidimarri R, “Evolutionary self-organising modelling of a municipal wastewater treatment plant”, Water Research, 37 (2003), pp 1199–1212.

    Article  Google Scholar 

  8. Roberts S.C. and Howard D, “Detection of incidents on motorways in low flow high speed conditions by genetic programming”, Cagnoni S et al (eds): EvoWorkshops 2002, LNCS 2279, Springer-Verlag, 2002, pp 245–254.

    Google Scholar 

  9. Dorado J et al, “Prediction and modelling of the flow of a typical urban basin through genetic programming”, Cagnoni S et al (eds): EvoWorkshops 2002, LNCS 2279, Springer-Verlag, 2002, pp 190–201.

    Google Scholar 

  10. Howard D and Roberts SC, “The prediction of journey times on motorways using genetic programming”, Cagnoni S et al (eds): EvoWorkshops 2002, LNCS 2279, Springer-Verlag, 2002, pp 210–221.

    Google Scholar 

  11. Ishino Y and Jin Y, “Estimate design intent: a multiple genetic programming and multivariate analysis based approach”, Advanced Engineering Infomatics, 16(2002), pp107–125.

    Article  Google Scholar 

  12. Babovic V et al, “A data mining approach to modelling of water supply assets”, Urban Water, 4(2002), pp401–414.

    Article  Google Scholar 

  13. Kojima F. et al, “Identification of crack profiles using genetic programming and fuzzy inference”, Journal of Materials Processing Technology, Elsevier, 108 (2001), pp263–267.

    Article  Google Scholar 

  14. Whigham P.A. and Crapper P.F, “Modelling rainfall-runoff using genetic programming”, Mathematical and Computer Modelling, 33(2001), pp707–721.

    Article  MATH  Google Scholar 

  15. Lee D.G et al, “Genetic programming model for long-term forecasting of electric power demand”, Electric power systems research, Elsevier, 40, 1997, pp17–22.

    Article  Google Scholar 

  16. Montana D.J. and Czerwinski S, “Evolving control laws for a network of traffic signals”, Proceedings of the Firs Annual Conference: Genetic Programming, July 28-3, 1996. Stanford University, pp333–338.

    Google Scholar 

  17. Köppen M and Nickolay B, “Design of image exploring agent using genetic programming”, Proceedings of IIZUKA’96 Japan, 1996, pp549–552.

    Google Scholar 

  18. Yang Y. and Soh C.K, “Automated optimum design of structures using genetic programming”, Computers and Structures, Pergamon, 80 (2002), pp1537–1546.

    Article  Google Scholar 

  19. MIT Trussworks.: 2002, Trussworks home page, [ONLINE], Available: http://web.mit.edu/emech/dontindex-build/java/trussworks/index.html [20/10/03].

    Google Scholar 

  20. Yang J and Soh C.K, “Structural optimization by genetic algorithms with tournament selection”, Journal of Computing in Civil Engineering, July 1997, pp195–200.

    Google Scholar 

  21. Diada J.M et al, “Visualizing tree structures in genetic programming”, Lecture Notes in Computer Science 2724, 2003, pp1652–1664.

    Google Scholar 

  22. Web3D Consortium.: 2003, X3D Working Group, [ONLINE], Available: http://www.web3d.org/x3d.html [13/10/03]

    Google Scholar 

  23. Wernert E.A and Hanson A.J, “Tethering and reattachment in collaborative virtual environments”, Proceedings of IEEE Virtual Reality 2000, IEEE Computer Society Press, 2000, pp292.

    Google Scholar 

  24. Watson A.H. and Parmee I.C., “Systems identification using genetic programming”, Proceedings of ACEDC’96, 1996.

    Google Scholar 

  25. Watson A.H. and Parmee I.C., “Improving engineering design models using an alternative genetic programming approach”, Proceedings of International Conference on adaptive computing in design and manufacture, 1998, pp193–206.

    Google Scholar 

  26. Hudson M.G. and Parmee I.C.: 1995, “The application of genetic algorithms to conceptual design”, Sharpe, J. (eds), AI System Support for Conceptual Design, Springer-Verlag, pp. 17-36.

    Google Scholar 

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© 2004 Springer-Verlag London

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Shaw, D., Miles, J., Gray, A. (2004). Genetic Programming within Civil Engineering. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_5

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  • DOI: https://doi.org/10.1007/978-0-85729-338-1_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-829-9

  • Online ISBN: 978-0-85729-338-1

  • eBook Packages: Springer Book Archive

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