Speeding Up Evolutionary Algorithms Through Restricted Mutation Operators

  • Benjamin Doerr
  • Nils Hebbinghaus
  • Frank Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


We investigate the effect of restricting the mutation operator in evolutionary algorithms with respect to the runtime behavior. For the Eulerian cycle problem; we present runtime bounds on evolutionary algorithms with a restricted operator that are much smaller than the best upper bounds for the general case. It turns out that a plateau that both algorithms have to cope with is left faster by the new algorithm. In addition, we present a lower bound for the unrestricted algorithm which shows that the restricted operator speeds up computation by at least a linear factor.


Evolutionary Algorithm Mutation Operator Search Point Current Path Jump Operator 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Giel, O., Wegener, I.: Evolutionary algorithms and the maximum matching problem. In: Alt, H., Habib, M. (eds.) STACS 2003. LNCS, vol. 2607, pp. 415–426. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Hierholzer, C.: Über die Möglichkeit, einen Linienzug ohne Wiederholung und ohne Unterbrechung zu umfahren. Math. Ann. 6, 30–32 (1873)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Jansen, T., Wegener, I.: Evolutionary algorithms — how to cope with plateaus of constant fitness and when to reject strings of the same fitness. IEEE Trans. on Evolutionary Computation 5, 589–599 (2001)CrossRefGoogle Scholar
  4. 4.
    Mattfeld, D.C., Bierwirth, C.: An efficient genetic algorithm for job shop scheduling with tardiness objectives. European Journal of Operational Research 155, 616–630 (2004)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Michalewicz, Z., Fogel, D.B.: How to solve it. Springer, Berlin (2004)MATHGoogle Scholar
  6. 6.
    Neumann, F.: Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 80–89. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Neumann, F.: Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. In: Proc. of the Congress on Evolutionary Computation 2004 (CEC 2004), vol. 1, pp. 904–910. IEEE Press, Los Alamitos (2004)CrossRefGoogle Scholar
  8. 8.
    Neumann, F., Wegener, I.: Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 713–724. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Raidl, G.R., Koller, G., Julstrom, B.A.: Biased mutation operators for subgraph-selection problems. IEEE Transactions on Evolutionary Computation (to appear, 2006)Google Scholar
  10. 10.
    Scharnow, J., Tinnefeld, K., Wegener, I.: Fitness landscapes based on sorting and shortest paths problems. In: Proc. of Parallel Problem Solving from Nature — PPSN VII. LNCS, vol. 2939, pp. 54–63. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Witt, C.: Worst-case and average-case approximations by simple randomized search heuristics. In: Diekert, V., Durand, B. (eds.) STACS 2005. LNCS, vol. 3404, pp. 44–56. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Benjamin Doerr
    • 1
  • Nils Hebbinghaus
    • 1
  • Frank Neumann
    • 2
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.Institut für Informatik und Praktische MathematikChristian-Albrechts-Universität zu KielKielGermany

Personalised recommendations