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Mathematical Problems in Engineering
Volume 2011, Article ID 872415, 17 pages
http://dx.doi.org/10.1155/2011/872415
Research Article

Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

1Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, P.O. Box 34185-1416, Iran
2Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran
3Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414, Azadi Avenue, Tehran 1458889694, Iran

Received 21 May 2011; Accepted 7 July 2011

Academic Editor: J. J. Judice

Copyright © 2011 Moslem Shahsavar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, Springer, Berlin, Germany, 2003. View at Zentralblatt MATH
  2. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Boston, Mass, USA, 1975. View at Zentralblatt MATH
  3. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, Mass, USA, 1989. View at Zentralblatt MATH
  4. K. Deb, “An introduction to genetic algorithms,” Sādhanā. Academy Proceedings in Engineering Sciences, vol. 24, no. 4-5, pp. 293–315, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. K. Deb, “Genetic algorithm in search and optimization: the technique and applications,” in Proceedings of the International Workshop on Soft Computing and Intelligent Systems, pp. 58–87, Calcutta, India, 1998.
  6. A. W. M. Ng and B. J. C. Perera, “Selection of genetic algorithm operators for river water quality model calibration,” Engineering Applications of Artificial Intelligence, vol. 16, no. 5-6, pp. 529–541, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. A. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124–141, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. H. P. Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, New York, NY, USA, 1981.
  9. K. A. de Jong and W. M. Spears, “An analysis of the interacting roles of population size and crossover in genetic algorithms,” in Proceedings of the International Conference on Parallel Problems Solving from Nature, pp. 38–47, Springer, Berlin, Germany, 1990.
  10. B. Friesleben and M. Hartfelder, “Optimization of genetic algorithms by genetic algorithms,” in Artificial Neural Networks and Genetic Algorithms, R. F. Albrecht, C. R. Reeves, and N. C. Steele, Eds., pp. 392–399, Springer, Belin, Germany, 1993. View at Google Scholar
  11. M. E. Samples, M. J. Byom, and J. M. Daida, “Parameter sweeps for exploring parameter spaces of genetic and evolutionary algorithms,” in Parameter Setting in Evolutionary Algorithms, F. G. Lobo, C.F. Lima, and Z. Michalewicz, Eds., pp. 161–184, Springer, Berlin, Germany, 2007. View at Google Scholar
  12. M. Preuss and T. Bartz-Beielstein, “Sequential parameter optimization applied to self-adaptation for binary-coded evolutionary algorithms,” in Parameter Setting in Evolutionary Algorithms, F. G. Lobo, C.F. Lima, and Z. Michalewicz, Eds., pp. 91–119, Springer, Berlin, Germany, 2007. View at Google Scholar
  13. T. P. Bagchi and K. Deb, “Calibration of GA parameters: the design of experiments approach,” Computer Science and Informatics, vol. 26, no. 3, pp. 45–56, 1996. View at Google Scholar
  14. R. Ruiz and C. Maroto, “A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility,” European Journal of Operational Research, vol. 169, no. 3, pp. 781–800, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. R. Ruiz, C. Maroto, and J. Alcaraz, “Two new robust genetic algorithms for the flowshop scheduling problem,” Omega, vol. 34, no. 5, pp. 461–476, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Saremi, T. Y. ElMekkawy, and G. G. Wang, “Tuning the parameters of a memetic algorithm to solve vehicle routing problem with backhauls using design of experiments,” International Journal of Operations Research, vol. 4, pp. 206–219, 2007. View at Google Scholar
  17. G. Taguchi and Y. Wu, Introduction to Off-Line Quality Control, Central Japan Quality Control Association, Nagoya, Japan, 1980.
  18. O. François and C. Lavergne, “Design of evolutionary algorithms—a statistical perspective,” IEEE Transactions on Evolutionary Computation, vol. 5, no. 2, pp. 129–148, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Czarn, C. MacNish, K. Vijayan, B. Turlach, and R. Gupta, “Statistical exploratory analysis of genetic algorithms,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 405–421, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. C. B. B. Costa, M. R. W. Maciel, and R. M. Filho, “Factorial design technique applied to genetic algorithm parameters in a batch cooling crystallization optimisation,” Computers and Chemical Engineering, vol. 29, no. 10, pp. 2229–2241, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. C. B. B. Costa, E. A. Ccopa-Rivera, M. C. A. F. Rezende, M. R. W. Maciel, and R. M. Filho, “Prior detection of genetic algorithm significant parameters: coupling factorial design technique to genetic algorithm,” Chemical Engineering Science, vol. 62, no. 17, pp. 4780–4801, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. F. G. Lobo, C. F. Lima, and Z. Michalewicz, Parameter Setting in Evolutionary Algorithms, Springer, Berlin, Germany, 2007.
  23. W. M. Jenkins, “Towards structural optimization via the genetic algorithm,” Computers and Structures, vol. 40, no. 5, pp. 1321–1327, 1991. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. P. Hajela, “Stochastic search in discrete structural optimization simulated annealing, genetic algorithms and neural networks,” in Discrete Structural Optimization, W. Gutkowski, Ed., pp. 55–134, Springer, New York, NY, USA, 1997. View at Google Scholar · View at Zentralblatt MATH
  25. G. R. Reeves, Modern Heuristic Techniques for Combinatorial Problems, John Wiley & Sons, New York, NY, USA, 1993.
  26. O. Andrzej, Evolutionary Algorithms for Single and Multicriteria Design Optimization, Physica, New York, NY, USA, 2002.
  27. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, London, UK, 1996.
  28. T. Back, D. B. Fogel, and Z. Michalewicz, Handbook of Evolutionary Computation, Institute of Physics Publishing, Bristol, UK, 1997. View at Publisher · View at Google Scholar
  29. D. E. Goldberg and K. Deb, “A comparative analysis of selection schemes used in genetic algorithms,” in Foundations of Genetic Algorithms, G. Rawlins, Ed., pp. 69–93, Morgan Kaufmann, San Mateo, Calif, USA, 1991. View at Google Scholar
  30. G. Syswerda, “Uniform crossover in genetic algorithms,” in Proceedings of the 3rd Conference on Genetic Algorithms, J. D. Schaffer, Ed., pp. 2–8, San Francisco, Calif, USA, 1989.
  31. L. Eshelman, R. Caruana, and D. Schaffer, “Biases in the crossover landscape,” in Proceedings of the 3rd Conference on Genetic Algorithms, J. D. Schaffer, Ed., pp. 10–19, San Francisco, Calif, USA, 1989.
  32. W. M. Spears and K. A. de Jong, “On the virtues of parameterized uniform crossover,” in Proceedings of the 4rd Conference on Genetic Algorithms, J. D. Schaffer, Ed., pp. 230–236, San Francisco, Calif, USA, 1991.
  33. K. A. de Jong and W. M. Spears, “A formal analysis of the role of multi-point crossover in genetic algorithms,” Annals of Mathematics and Artificial Intelligence, vol. 5, no. 1, pp. 1–26, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  34. D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, NY, USA, 6th edition, 2005.
  35. R. H. Myers and D. C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, New York, NY, USA, 2002.
  36. R. Narasimhan, “Goal programming in a fuzzy environment,” Decision Sciences, vol. 11, pp. 325–336, 1980. View at Publisher · View at Google Scholar
  37. R. N. Tiwari, S. Dharmar, and J. R. Rao, “Fuzzy goal programming—an additive model,” Fuzzy Sets and Systems, vol. 24, no. 1, pp. 27–34, 1987. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  38. J. K. Lee and Y. D. Kim, “Search heuristics for resource constrained project scheduling,” Journal of the Operational Research Society, vol. 47, no. 5, pp. 678–689, 1996. View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  39. L. Özdamar, “A genetic algorithm approach to a general category project scheduling problem,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 29, no. 1, pp. 44–59, 1999. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Hartmann, “A competitive genetic algorithm for resource-constrained project scheduling,” Naval Research Logistics, vol. 45, no. 7, pp. 733–750, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  41. J. Alcaraz and C. Maroto, “A robust genetic algorithm for resource allocation in project scheduling,” Annals of Operations Research, vol. 102, pp. 83–109, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  42. J. F. Gonçalves, J. J. M. Mendes, and M. G. C. Resende, “A genetic algorithm for the resource constrained multi-project scheduling problem,” European Journal of Operational Research, vol. 189, no. 3, pp. 1171–1190, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  43. A. A. Najafi and S. T. A. Niaki, “A genetic algorithm for resource investment problem with discounted cash flows,” Applied Mathematics and Computation, vol. 183, no. 2, pp. 1057–1070, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  44. A. A. Najafi, S. T. A. Niaki, and M. Shahsavar, “A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations,” Computers and Operations Research, vol. 36, no. 11, pp. 2994–3001, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus