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Computational Intelligence and Neuroscience
Volume 2016, Article ID 8289237, 11 pages
http://dx.doi.org/10.1155/2016/8289237
Research Article

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

1Universidad Nacional de la Patagonia Austral, Ruta 3 Acceso Norte, s/n, Caleta Olivia, 9011 Santa Cruz, Argentina
2Universidad de Málaga, Campus Teatinos, 29071 Málaga, Spain
3Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina

Received 29 January 2016; Accepted 15 May 2016

Academic Editor: Marc Van Hulle

Copyright © 2016 Andrea Villagra 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. G. Dounias and V. Vassiliadis, “Algorithms and methods inspired from nature for solving supply chain and logistics optimization problems: a survey,” International Journal of Natural Computing Research, vol. 4, no. 3, pp. 26–51, 2014. View at Publisher · View at Google Scholar
  2. S. Mahdavi, M. E. Shiri, and S. Rahnamayan, “Metaheuristics in large-scale global continues optimization: a survey,” Information Sciences, vol. 295, pp. 407–428, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  3. N. Xiong, D. Molina, M. L. Ortiz, and F. Herrera, “A walk into metaheuristics for engineering optimization: principles, methods and recent trends,” International Journal of Computational Intelligence Systems, vol. 8, no. 4, pp. 606–636, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Blum, J. Puchinger, G. R. Raidl, and A. Roli, “Hybrid metaheuristics in combinatorial optimization: a survey,” Applied Soft Computing Journal, vol. 11, no. 6, pp. 4135–4151, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Raidl, “A unified view on hybrid metaheuristics,” in Hybrid Metaheuristics, pp. 1–12, Springer, 2006. View at Google Scholar
  6. E.-G. Talbi, “A taxonomy of hybrid metaheuristics,” Journal of Heuristics, vol. 8, no. 5, pp. 541–564, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Blum, A. Roli, and E. Alba, “Chapter 1. An introduction to metaheuristic techniques,” in Parallel Metaheuristics: A New Class of Algorithms, vol. 47, pp. 3–42, John Wiley & Sons, New York, NY, USA, 2005. View at Publisher · View at Google Scholar
  8. C. Cotta-Porras, “Study of hybridization techniques and their application to the design of evolutionary algorithms,” AI Communications, vol. 11, no. 3, pp. 223–224, 1998. View at Google Scholar · View at Scopus
  9. M. El-Abd and M. Kamel, “A taxonomy of cooperative search algorithms,” in Hybrid Metaheuristics, pp. 32–41, Springer, 2005. View at Google Scholar
  10. J. Puchinger and G. Raidl, “Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification,” in Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, pp. 41–53, Springer, Berlin, Germany, 2005. View at Google Scholar
  11. A. Villagra, G. Leguizamón, and E. Alba, “Active components of metaheuristics in cellular genetic algorithms,” Soft Computing, vol. 19, no. 5, pp. 1295–1309, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Glover, “Heuristics for integer programming using surrogate constraints,” Decision Sciences, vol. 8, no. 1, pp. 156–166, 1977. View at Publisher · View at Google Scholar
  13. E. Alba and B. Dorronsoro, Cellular Genetic Algorithms, Springer, New York, NY, USA, 2008.
  14. M. Tomassini, “The parallel genetic cellular automata: application to global function optimization,” in Artificial Neural Nets and Genetic Algorithms, R. F. Albrecht, C. R. Reeves, and N. C. Steele, Eds., pp. 385–391, Springer, Heidelberg, Germany, 1993. View at Publisher · View at Google Scholar
  15. D. Whitley, “Cellular genetic algorithms,” in Proceedings of the 5th International Conference on Genetic Algorithms (ICGA '93), S. Forrest, Ed., p. 658, Morgan Kaufmann, San Diego, Calif, USA, 1993.
  16. F. Glover and G. Kochenberger, Handbook of Meta-heuristics, Springer Science & Business Media, Boston, Mass, USA, 2003.
  17. M. Gendreau and J. Y. Potvin, Eds., Handbook of Metaheuristics, Springer, New York, NY, USA, 2010.
  18. D. Goldberg, K. Deb, and J. Horn, “Massive multimodality, deception, and genetic algorithms,” in Proceedings of the International Conference Parallel Problem Solving from Nature II, R. Manner and B. Manderick, Eds., pp. 37–46, 1992.
  19. S. Tsutsui and Y. Fujimoto, “Forking genetic algorithm with blocking and shrinking modes,” in Proceedings of the 5th International Conference on Genetic Algorithms, S. Forrest, Ed., pp. 206–213, Morgan Kaufmamann, 1993.
  20. K. De Jong, M. Potter, and W. Spears, “Using problem generators to explore the effects of epistasis,” in Proceedings of the 7th International Conference on Genetic Algorithms, pp. 338–345, Morgan Kaufmann, 1997.
  21. S. Droste, T. Jansen, and I. Wegener, “A natural and simple function which is hard for all evolutionary algorithms,” in Proceedings of the 3rd Asia-Pacific Conference on Simulted Evolution and Learning (SEAL '00), pp. 2704–2709, 2000.
  22. C. H. Papadimitriou, Computational Complexity, Addison-Wesley, 1994. View at MathSciNet
  23. F. MacWilliams and N. Sloane, The Theory of Error-Correcting Codes: Part 2, vol. 16, Elsevier, 1977.
  24. S. Khuri, T. Bäck, and J. Heitkötter, “An evolutionary approach to combinatorial optimization problems,” in Proceedings of the 22nd Annual ACM Computer Science Conference, pp. 66–73, Phoenix, Ariz, USA, March 1994. View at Publisher · View at Google Scholar
  25. D. Stinson, An Introduction to the Design and Analysis of Algorithms, The Charles Babbage Research Centre, St. Pierre, Canada, 1985.
  26. J. Schaffer and L. Eshelman, “On crossover as an evolutionary viable strategy,” in Proceedings of the 4th 4th International Conference on Genetic Algorithms (ICGA '91), R. K. Belew and L. B. Booker, Eds., pp. 61–68, Morgan Kaufmann, San Diego, Calif, USA, July 1991.
  27. S. Martello and P. Toth, “Worst-case analysis of greedy algorithms for the subset-sum problem,” Mathematical Programming, vol. 28, no. 2, pp. 198–205, 1984. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  28. S. García, A. Fernández, J. Luengo, and F. Herrera, “Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power,” Information Sciences, vol. 180, no. 10, pp. 2044–2064, 2010. View at Publisher · View at Google Scholar · View at Scopus