Hierarchical Genetic Algorithms

  • Edwin D. de Jong
  • Dirk Thierens
  • Richard A. Watson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)


Current Genetic Algorithms can efficiently address order-k separable problems, in which the order of the linkage is restricted to a low value k. Outside this class, there exist hierarchical problems that cannot be addressed by current genetic algorithms, yet can be addressed efficiently in principle by exploiting hierarchy. We delineate the class of hierarchical problems, and describe a framework for Hierarchical Genetic Algorithms. Based on this outline for algorithms, we investigate under what conditions hierarchical problems may be solved efficiently. Sufficient conditions are provided under which hierarchical problems can be addressed in polynomial time. The analysis points to the importance of efficient sampling techniques that assess the quality of module settings.


  1. 1.
    Goldberg, D.E.: The design of innovation. Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar
  2. 2.
    Simon, H.A.: The Sciences of the Artificial. The MIT Press, Cambridge (1968)Google Scholar
  3. 3.
    Watson, R.A., Hornby, G.S., Pollack, J.B.: Modeling building-block interdependency. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 97–106. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 511–518. Morgan Kaufmann, San Francisco (2001)Google Scholar
  5. 5.
    Pelikan, M., Goldberg, D.E.: Hierarchical problem solving by the bayesian optimization algorithm. In: Whitley, D., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, Nevada, USA, pp. 267–274. Morgan Kaufmann, San Francisco (2000)Google Scholar
  6. 6.
    Watson, R.A.: Compositional Evolution: Interdisciplinary Investigations in Evolvability, Modularity, and Symbiosis. PhD thesis, Brandeis University (2002)Google Scholar
  7. 7.
    Watson, R.A., Pollack, J.B.: A computational model of symbiotic composition in evolutionary transitions. Biosystems 69, 187–209 (2003); Special Issue on Evolvability, ed. NehanivCrossRefGoogle Scholar
  8. 8.
    De Jong, E.D., Thierens, D.: Exploiting modularity, hierarchy, and repetition in variable-length problems. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 1030–1041. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Hu, J., Goodman, E.D.: The hierarchical fair competition (HFC) model for parallel evolutionary algorithms. In: Fogel, D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow, P., Shackleton, M. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 49–54. IEEE Press, Los Alamitos (2002)Google Scholar
  10. 10.
    Gulsen, M., Smith, A.E.: A hierarchical genetic algorithm for system identification and curve fitting with a supercomputer implementation. In: Davis, L.D., et al. (eds.) Evolutionary Algorithms, pp. 111–137. Springer, New York (1999)Google Scholar
  11. 11.
    Tang, K., Man, K., Istepanian, R.: Teleoperation controller design using hierarical genetic algorithms. In: Proceedings of the IEEE International conference on Industrial Technology, pp. 707–711 (2000)Google Scholar
  12. 12.
    Thierens, D., Goldberg, D.: Mixing in genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 38–45. Morgan Kaufmann, San Francisco (1993)Google Scholar
  13. 13.
    Thierens, D.: Scalability problems of simple genetic algorithms. Evolutionary Computation 7, 331–352 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Edwin D. de Jong
    • 1
  • Dirk Thierens
    • 1
  • Richard A. Watson
    • 2
  1. 1.Decision Support Systems GroupUniversiteit UtrechtThe Netherlands
  2. 2.Dept. of Organismic and Evolutionary BiologyHarvard UniversityCambridgeUSA

Personalised recommendations