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Mathematical Problems in Engineering
Volume 2013, Article ID 132697, 11 pages
http://dx.doi.org/10.1155/2013/132697
Research Article

Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming

Department of Information Management, National Chi Nan University, Nantou 545, Taiwan

Received 10 May 2013; Accepted 20 September 2013

Academic Editor: Yang Xu

Copyright © 2013 Peng-Yeng Yin and Hsi-Li Wu. 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. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, 1975.
  2. S. Baluja, “Population based incremental learning: a method for integrating genetic search based function optimization and competitive learning,” Tech. Rep. CMU-CS-94-163, Carnegie Mellon University, 1994. View at Google Scholar
  3. H. Mühlenbein and G. Paaß, “From recombination of genes to the estimation of distributions I. Binary parameters,” in Proceedings of the 4th Parallel Problem Solving from Nature (PPSN '96), vol. 4, pp. 178–187, 1996.
  4. F. Glover, “Tabu search and adaptive memory programming—advances, applications and challenges,” in Interfaces in Computer Science and Operations Research, H. Barr and J. L. Kennington, Eds., pp. 1–75, Kluwer Academic Publishers, 1996. View at Google Scholar
  5. M. Laguna and R. Marti, Scatter Search: Methodology and Implementation in C, Kluwer Academic Publishers, London, UK, 2003.
  6. F. Glover, “A template for scatter search and path relinking,” in Artificial Evolution, vol. 1363 of Lecture Notes in Computer Science, pp. 13–54, 1998. View at Google Scholar
  7. F. Glover, “Ejection chains, reference structures and alternating path methods for traveling salesman problems,” Discrete Applied Mathematics, vol. 65, no. 1–3, pp. 223–253, 1996. View at Google Scholar · View at Scopus
  8. T. A. Feo and M. G. C. Resende, “Greedy randomized adaptive search procedures,” Journal of Global Optimization, vol. 6, no. 2, pp. 109–133, 1995. View at Publisher · View at Google Scholar · View at Scopus
  9. P.-Y. Yin, F. Glover, M. Laguna, and J.-X. Zhu, “Cyber swarm algorithms—improving particle swarm optimization using adaptive memory strategies,” European Journal of Operational Research, vol. 201, no. 2, pp. 377–389, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Nakano, A. Ishigame, and K. Yasuda, “Particle swarm optimization based on the concept of tabu search,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC '07), pp. 3258–3263, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Taillard and L. Gambardella, “Adaptive memories for the quadratic assignment problems,” Tech. Rep., Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale (IDSIA), 1997. View at Google Scholar
  12. P. Y. Yin, “Towards more effective metaheuristic computing,” in Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, P. Y. Yin, Ed., IGI-Global Publishing, February 2012. View at Google Scholar
  13. P.-Y. Yin and E.-P. Su, “Cyber Swarm optimization for general keyboard arrangement problem,” International Journal of Industrial Ergonomics, vol. 41, no. 1, pp. 43–52, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. P.-Y. Yin, F. Glover, M. Laguna, and J.-X. Zhu, “Cyber swarm algorithms—improving particle swarm optimization using adaptive memory strategies,” European Journal of Operational Research, vol. 201, no. 2, pp. 377–389, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Y. Yin and Y. T. Chiang, “Cyber swarm algorithms for multi-objective nurse rostering problem,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 5, pp. 2043–2063, 2013. View at Google Scholar
  16. J. Grahl, S. Minner, and P. A. N. Bosman, “Learning structure illuminates black boxes: an introduction into estimation of distribution algorithms,” in Advances in Metaheuristics for Hard Optimization, Z. Michalewicz and P. Siarry, Eds., pp. 365–396, Springer, Berlin, Germany, 2008. View at Google Scholar
  17. G. R. Harik, F. G. Lobo, and D. E. Goldberg, “Compact genetic algorithm,” in Proceedings of IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 523–528, May 1998. View at Scopus
  18. S. J. De Bonet, C. L. Isbell, and P. Viola, “MIMIC: finding optima by estimating probability densities,” in Advances in Neural Information Processing Systems, M. C. Mozer, M. I. Jordan, and T. Petsche, Eds., vol. 9, p. 424, The MIT Press, 1997. View at Google Scholar
  19. S. Baluja and S. Davies, “Using optimal dependency-trees for combinatorial optimization: learning the structure of the search space,” in Proceedings of the International Conference on Machine Learning, D. H. Fisher, Ed., pp. 30–38, 1997.
  20. M. Pelikan and H. Mühlenbein, “The bivariate marginal distribution algorithm,” in Advances in Soft Computing—Engineering Design and Manufacturing, R. Roy, T. Furuhashi, and P. K. Chawdhry, Eds., pp. 521–535, 1999. View at Google Scholar
  21. G. Harik, “Linkage learning via probabilistic modeling in the ECGA,” Tech. Rep. 99010, IlliGAL, University of Illinois, Urbana, Ill, USA, 1999. View at Google Scholar
  22. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz, BOA: The Bayesian Optimization Algorithm, 1999.
  23. M. Pelikan, K. Sastry, M. V. Butz, and D. E. Goldberg, “Hierarchical BOA on random decomposable problems,” IlliGAL Report 2006002, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign, Ill, USA, 2006. View at Google Scholar
  24. R. Exteberria and P. Larrañaga, “Global optimization using bayesian networks,” in Proceedings of the 2nd Symposium on Artificial Intelligence (CIMAF '1999), pp. 332–339, 1999.
  25. H. Mühlenbein and T. Mahnig, “FDA—a scalable evolutionary algorithm for the optimization of additively decomposed functions,” Evolutionary Computation, vol. 7, no. 4, pp. 353–376, 1999. View at Google Scholar · View at Scopus
  26. M. Sebag and A. Ducoulombier, “Extending population-based incremental learning to continuous search spaces,” in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN '98), vol. 5, pp. 418–427, 1998.
  27. P. Larranaga, R. Etxeberria, J. A. Lozano, and J. M. Pena, “Optimization in continuous domains by learning and simulation of Gaussian networks,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '00), pp. 201–204, 2000.
  28. P. Larrañaga, R. Etxeberria, J. A. Lozano, and J. M. Pena, “Optimization by learning and simulation of Bayesian and Gaussian networks,” Tech. Rep. EHU-kZAA-IK-4/99, University of the Basque Country, 1999. View at Google Scholar
  29. P. A. N. Bosman and D. Thierens, “Expanding from discrete to continuous estimation of distribution algorithms: the IDEA,” in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature (PPSN '00), vol. 6, pp. 767–776, 2000.
  30. H. Handa, “Estimation of distribution algorithms with niche separation mechanism,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC '07), pp. 119–126, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. C. W. Ahn and H.-T. Kim, “Estimation of particle swarm distribution algorithms: bringing together the strengths of PSO and EDAs,” in Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference (GECCO '09), pp. 1817–1818, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. Q. Zhang, J. Sun, E. Tsang, and J. Ford, “Hybrid estimation of distribution algorithm for global optimization,” Engineering Computations, vol. 21, no. 1, pp. 91–107, 2004. View at Google Scholar · View at Scopus
  33. F. Glover, “Future paths for integer programming and links to artificial intelligence,” Computers and Operations Research, vol. 13, no. 5, pp. 533–549, 1986. View at Google Scholar · View at Scopus
  34. S. Kessentini, D. Barchiesi, T. Grosges, L. Giraud-Moreau, and M. L. de la Chapelle, “Adaptive non-uniform particle swarm application to plasmonic design,” International Journal of Applied Metaheuristic Computing, vol. 2, no. 1, pp. 18–28, 2011. View at Google Scholar
  35. G. Maquera, M. Laguna, D. A. Gandelman, and A. P. Sant'Anna, “Scatter search applied to the vehicle routing problem with simultaneous delivery and pickup,” International Journal of Applied Metaheuristic Computing, vol. 2, no. 2, pp. 1–20, 2011. View at Google Scholar
  36. M. Hassannezhad and N. Javadian, “Utilizing the modified self-adaptive differential evolution algorithm in dynamic cellular manufacturing system,” International Journal of Applied Metaheuristic Computing, vol. 3, no. 2, pp. 1–17, 2012. View at Google Scholar
  37. P. Joshi and P. Kulkarni, “Incremental learning: areas and methods—a survey,” International Journal of Data Mining & Knowledge Management Process, vol. 2, no. 5, pp. 43–51, 2012. View at Google Scholar