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Evolving Crossover Operators for Function Optimization

  • Laura Dioşan
  • Mihai Oltean
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)

Abstract

A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded into a standard Genetic Algorithm which is used for solving a particular problem. Several crossover operators for function optimization are evolved using the considered model. The evolved crossover operators are compared to the human-designed convex crossover. Numerical experiments show that the evolved crossover operators perform similarly or sometimes even better than standard approaches for several well-known benchmarking problems.

Keywords

Genetic Algorithm Crossover Operator Function Optimization Genetic Operator Cardinality Constraint 
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.

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References

  1. 1.
    Angeline, P.J.: Two self-adaptive crossover operators for genetic programming. In: Advances in Genetic Programming II, pp. 89–110. MIT Press, Cambridge (1996)Google Scholar
  2. 2.
    Bremermann, H.J.: Optimization through evolution and recombination. In: Yovits, M.C., Jacobi, G.T., Goldstein, G.D. (eds.) Proceedings of the Conference on Self-Organizing Systems, Chicago, Illinois, May 22-24, Self-Organizing Systems 1962, pp. 93–106 (1962)Google Scholar
  3. 3.
    Chang, T.-J., Meade, N., Beasley, J.E., Sharaiha, Y.M.: Heuristics for cardinality constrained portfolio optimisation, Comp. & Opns. Res, vol. 27, pp. 1271–1302 (2000)Google Scholar
  4. 4.
    Edmonds, B.: Meta-genetic programming: coevolving the operators of variation. Electrik on AI 9, 13–29 (2001)Google Scholar
  5. 5.
    Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic programming, On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  7. 7.
    Markowitz, H.: Portfolio Selection. Journal of Finance 7, 77–91 (1952)Google Scholar
  8. 8.
    Oltean, M., Grosan, C.: Evolving EAs using Multi Expression Programming. In: European Conference on Artificial Life, pp. 651–658. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Schwefel, H.-P.: Numerical optimization of computer models. John Wiley & Sons, New York (1981)MATHGoogle Scholar
  10. 10.
    Spector, L., Robinson, A.: Genetic Programming and Autoconstructive Evolution with the Push Programming Language. In: Genetic Programming and Evolvable Machines, (1), pp. 7–40. Kluwer, Dordrecht (2002)Google Scholar
  11. 11.
    Streichert, F., Ulmer, H., Zell, A.: Comparing discrete and continuous genotypes on the constrained portfolio selection problem. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1239–1250. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Tavares, J., Machado, P., Cardoso, A., Pereira, F.-B., Costa, E.: On the evolution of evolutionary algorithms. In: Keijzer, M., et al. (eds.) European Conference on Genetic Programming, pp. 389–398. Springer, Berlin (2004)CrossRefGoogle Scholar
  13. 13.
    Teller, A.: Evolving programmers: the co-evolution of intelligent recombination operators. In: Advances in Genetic Programming II, pp. 45–68. MIT Press, USA (1996)Google Scholar
  14. 14.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transaction on Evolutionary Computation, 82–102 (1999)Google Scholar
  15. 15.
    Wolpert, D.H., McReady, W.G.: No Free Lunch Theorems for Search., Technical Report SFI-TR-05-010, Santa Fe Institute, USA (1995)Google Scholar
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Laura Dioşan
    • 1
  • Mihai Oltean
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania

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