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Cultural Operators for a Quantum-Inspired Evolutionary Algorithm Applied to Numerical Optimization Problems

  • André V. Abs da Cruz
  • Marco Aurélio C. Pacheco
  • Marley Vellasco
  • Carlos R. Hall Barbosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3562)

Abstract

This work presents the application of cultural algorithms operators to a new quantum-inspired evolutionary algorithm with numerical representation. These operators (fission, fusion, generalization and specialization) are used in order to provide better control over the quantum-inspired evolutionary algorithm. We also show that the quantum-inspired evolutionary algorithm with numerical representation behaves in a very similar manner to a pure cultural algorithm and we propose further investigations concerning this aspect.

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References

  1. 1.
    Shor, P.W.: Algorithms for quantum computation: Discrete log and factoring. In: Proc. 35th Ann. Symp. Foundations of Computer Science, pp. 124–134. IEEE Computer Society Press, Los Alamitos (1994)CrossRefGoogle Scholar
  2. 2.
    Shor, P.W.: Quantum computing. Documenta Mathematica, 467–486 (1998)Google Scholar
  3. 3.
    Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing (STOC), pp. 212–219. ACM Press, New York (1996)Google Scholar
  4. 4.
    Spector, L., Barnum, H., Bernstein, H.J., Swami, N.: Finding a better-than-classical quantum AND/OR algorithm using genetic programming. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 2239–2246. IEEE Press, Los Alamitos (1999)Google Scholar
  5. 5.
    Han, K.H., Kim, J.H.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 1354–1360. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  6. 6.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6, 580–593 (2002)CrossRefGoogle Scholar
  7. 7.
    Narayanan, A., Moore, M.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC 1996), pp. 61–66. IEEE Press, Los Alamitos (1996)CrossRefGoogle Scholar
  8. 8.
    Abs da Cruz, A.V., Vellasco, M.M.B.R., Pacheco, M.A.C., Hall Barbosa, C.R.: Quantum-inspired evolutionary algorithms and its application to numerical optimization problems. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 212–217. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, River Edge (1994)Google Scholar
  10. 10.
    Chung, C., Reynolds, R.G.: A testbed for solving optimization problems using cultural algorithms. In: Proceedings of EP 1996 (1996)Google Scholar
  11. 11.
    Bersini, H., Dorigo, M., Langerman, S., Seront, G., Gambardella, L.: Results of the first international contest on evolutionary optimisation (1st iceo). In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC 1996), pp. 622–627. IEEE Press, Los Alamitos (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • André V. Abs da Cruz
    • 1
  • Marco Aurélio C. Pacheco
    • 1
  • Marley Vellasco
    • 1
  • Carlos R. Hall Barbosa
    • 1
  1. 1.ICA — Applied Computational Intelligence Lab, Electrical Engineering DepartmentPontifícia Universidade Católica do Rio de Janeiro 

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