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Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved Engineering Designs

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

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Abstract

Optimization is being increasing applied to engineering design problems throughout the world. iSIGHT is a generic engineering design environment that provides engineers with an optimization toolkit of leading optimization algorithms and an optimization advisor to solve their optimization needs. This paper focuses on the key role played by the toolkit’s genetic algorithm in providing a robust, general purpose solution to nonlinear continuous, mixed integer nonlinear and integer combinatorial problems. The robustness of the genetic algorithm is demonstrated on successful application to 30 engineering benchmark problems and the following three real world problems: a marine naval propeller, a heart pacemaker and a jet engine turbine airfoil.

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References

  1. Engineous Software Incorporated. www.engineous.com

    Google Scholar 

  2. Vanderplaats, G.: Numerical Optimization Techniques for Engineering Design. (1999).

    Google Scholar 

  3. Belegund, A. and Chandrupatla, T.: Optimization Concepts and Applications in Engineering. Prentice Hall (1999)

    Google Scholar 

  4. Onwubiko, C.: Introduction to Engineering Design Optimization. Prentice Hall (2000)

    Google Scholar 

  5. Gen, M. and Cheng, R: Genetic Algorithms and Engineering Optimization. John Wiley & Sons (2000).

    Google Scholar 

  6. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons (2001).

    Google Scholar 

  7. Back, T.: A Users Guide to GENEsYs 1.0. University of Dortumnd. (1992)

    Google Scholar 

  8. Hiroyasu, T.: Spec Sheet: Distributed Genetic Algorithms ga2k (ver 1.1). Intelligent Systems Design Lab. Doushisya University (2002)

    Google Scholar 

  9. Inger, L.: Adaptive Simulated Annealing. http://www.ingber.com/. (2002)

    Google Scholar 

  10. Vanderplaats, G.: ADS — A Fortran Program for Automated Design Synthesis. Santa Barbara, CA: Engineering Design Optimization, Inc. (1988)

    Google Scholar 

  11. Johnson, M.: Hooke and Jeeves Algorithm. http://www.netlib.org/opt/hooke.c (1994)

    Google Scholar 

  12. Spellucci, P.: Donlp2 User Guide. http://www.netlib.org/opt/donlp2/donlp2doc.ps

    Google Scholar 

  13. Schittkowski, K.: NLPQL: A Fortran subroutine for solving constrained non linear programs. Annals of Operations Research, Vol.5, (1985–1986) 4850–500

    MathSciNet  Google Scholar 

  14. Tseng, C.: MOST 1.1. Applied Optimal Design Laboratory, National Chiao Tung Univeristy, Technical Report AODL-9-01 (1996)

    Google Scholar 

  15. Smith, S. and Lasdon, L.: Solving large sparse nonlinear programs using GRG. ORSA J. Comput. 4, (1992) 1–15

    MathSciNet  Google Scholar 

  16. Tanese, R.: Distributed genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms, (1989) 434–439.

    Google Scholar 

  17. Sandgren, E.: The utility of nonlinear programming algorithms. Purdue University Ph.D. Thesis, West Lafayette, IN (1977).

    Google Scholar 

  18. Sandgren, E.: Nonlinear integer and discrete programming in mechanical design optimization. Transactions of the ASME, Journal of Mechanical Design, 112(2), (1990) 223–229

    Article  Google Scholar 

  19. Fylstra, D., Lasdon, L., Watson, J. and Waren, A.: Design and Use of the Microsoft Excel Solver. Interfaces 28 (1998) 29–55.

    Article  Google Scholar 

  20. Furse, C.: Design an Antenna for Pacemaker Communication. Microwaves & RF, March (2000)

    Google Scholar 

  21. Powell, D.: Inter-GEN: A hybrid approach to engineering design optimization. Rensselaer Polytechnic Institute Ph.D. Thesis, Troy, NY (1990).

    Google Scholar 

  22. Vanderplaats, G.: Numerical Optimization Techniques for Engineering Design. (1999) 317–320.

    Google Scholar 

  23. Powell, D., Skolnick, M., and Tong, S.: Interdigitation: A Hybrid Technique for Engineering Design Optimization. Handbook of Genetic Algorithms. Van Nostrand Reinhold. (1991) 312–331.

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Tong, S., Powell, D.J. (2003). Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved Engineering Designs. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_127

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  • DOI: https://doi.org/10.1007/3-540-45110-2_127

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

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