Advertisement

Extending the GA-EDA Hybrid Algorithm to Study Diversification and Intensification in GAs and EDAs

  • V. Robles
  • J. M. Peña
  • M. S. Pérez
  • P. Herrero
  • O. Cubo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)

Abstract

Hybrid metaheuristics have received considerable interest in recent years. Since several years ago, a wide variety of hybrid approaches have been proposed in the literature including the new GA-EDA approach. We have design and implemented an extension to this GA-EDA approach, based on statistical significance tests. This approach had allowed us to make an study of the balance of diversification (exploration) and intensification (exploitation) in Genetic Algorithms and Estimation of Distribution Algorithms.

Keywords

Local Search Hybrid Algorithm Distribution Algorithm Participation Ratio Hybrid Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bachelet, V., Talbi, E.: Cosearch: A co-evolutionary metaheuritics. In: Proceedings of Congress on Evolutionary Computation CEC 2000, pp. 1550–1557 (2000)Google Scholar
  2. 2.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  3. 3.
    Branin, F.K.: A widely convergent method for finding multiple solutions of simultaneous nonlinear equations. IBM Journal of Research and Development, 504–522 (1972)Google Scholar
  4. 4.
    Denzinger, J., Offerman, T.: On cooperation between evolutionary algorithms and other search paradigms. In: Proceedings of Congress on Evolutionary Computation CEC– 1999, pp. 2317–2324 (1999)Google Scholar
  5. 5.
    Foccaci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 57, Kluwer Academic Publishers, NorwellGoogle Scholar
  6. 6.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)zbMATHGoogle Scholar
  7. 7.
    Hao, J., Lardeux, F., Saubion, F.: A hybrid genetic algorithm for the satisfiability problem. In: Proceedings of the First International Workshop on Heuristics, Beijing (2002)Google Scholar
  8. 8.
    Herrera, F., Lozano, M.: Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation 4(1) (2000)Google Scholar
  9. 9.
    Holland, J.H.: Adaption in natural and artificial systems. The University of Michigan Press, Ann Harbor (1975)zbMATHGoogle Scholar
  10. 10.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Larrañaga, P., Etxeberria, R., Lozano, J.A., Peña, J.M.: Optimization in continuous domains by learning and simulation of Gaussian networks. In: Wu, A.S. (ed.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, pp. 201–204 (2000)Google Scholar
  12. 12.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  13. 13.
    Lin, F.T., Kao, C.Y., Hsu, C.C.: Incorporating genetic algorithms into simulated annealing. Proceedings of the Fourth International Symposium on Artificial Intelligence, 290–297 (1991)Google Scholar
  14. 14.
    Martin, O.C., Otto, S.W.: Combining simulated annealing with local search heuristics. Annals of Operations Research 63, 57–75 (1996)zbMATHCrossRefGoogle Scholar
  15. 15.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)zbMATHGoogle Scholar
  16. 16.
    Peña, J.M., Robles, V., Larrañaga, P., Herves, V., Rosales, F., Pérez, M.S.: GA-EDA: Hybrid evolutionary algorithm using genetic and estimation of distribution algorithms. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 361–371. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Robles, V., Peña, J.M., Larrañaga, P., Pérez, M.S., Herves, V.: GA-EDA: A new hybrid cooperative search evolutionary algorithm. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towars a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms. Springer, Heidelberg (2005) (in press)Google Scholar
  18. 18.
    Robles, V., Pérez, M.S., Herves, V., Peña, J.M., Larrañaga, P.: Parallel stochastic search for protein secondary structure prediction, Czestochowa, Poland. LNCS (2003)Google Scholar
  19. 19.
    Talbi, E.-G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)CrossRefGoogle Scholar
  20. 20.
    Törn, A., Ali, M.M., Viitanen, S.: Stochastic global optimization: Problem classes and solution techniques. Journal of Global Optimization 14 (1999)Google Scholar
  21. 21.
    Toulouse, M., Crainic, T., Sansó, B.: An experimental study of the systemic behavior of cooperative search algorithms. In: Osman., I., Voß, S., Martello, S., Roucairol, C. (eds.) In Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, vol. 26, pp. 373–392. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • V. Robles
    • 1
  • J. M. Peña
    • 1
  • M. S. Pérez
    • 1
  • P. Herrero
    • 2
  • O. Cubo
    • 1
  1. 1.Departamento de Arquitectura y Tecnología de Sistemas InformáticosUniversidad Politécnica de MadridMadridSpain
  2. 2.Departamento de Lenguajes y SistemasUniversidad Politécnica de MadridMadridSpain

Personalised recommendations