A Gene Based Adaptive Mutation Strategy for Genetic Algorithms

  • Sima Uyar
  • Sanem Sariel
  • Gulsen Eryigit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3103)


In this study, a new mechanism that adapts the mutation rate for each locus on the chromosomes, based on feedback obtained from the current population is proposed. Through tests using the one-max problem, it is shown that the proposed scheme improves convergence rate. Further tests are performed using the 4-Peaks and multiple knapsack test problems to compare the performance of the proposed approach with other similar parameter control approaches. A convergence control scheme that provides acceptable performance is chosen to maintain sufficient diversity in the population and implemented for all tested methods to provide fair comparisons. The effects of using a convergence control mechanism are not within the scope of this paper and will be explored in a future study. As a result of the tests, promising results which promote further experimentation are obtained.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angeline, P.J.: Adaptive and Self-adaptive Evolutionary Computation. Computational Intelligence. A Dynamic System Perspective, IEEE, 152–161 (1995)Google Scholar
  2. 2.
    Bäck, T.: Optimal Mutation Rates in Genetic Search. In: Proc. of 5th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco (1993)Google Scholar
  3. 3.
    Bäck, T., Schlütz, M.: Intelligent Mutation Rate Control in Canonical Genetic Algorithms. In: Proc. Int. Symp. on Methodologies for Intelligent Syst., pp. 158–167 (1996)Google Scholar
  4. 4.
    Baluja, S., Caruana, R.: Removing the Genetics from the Standard Genetic Algorithm. In: Proc. 12. Int. Conf. on Machine Learning, pp. 38–46. Morgan Kaufmann, San Francisco (1995)Google Scholar
  5. 5.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  6. 6.
    Gottlieb, J.: On the feasibility problem of penalty-based evolutionary algorithms for knapsack problems. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 50–59. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Hinterding, R., Gielewski, H., Peachey, T.C.: The Nature of Mutation in Genetic Algorithms. In: Proc. 6. Int. Conf. on GAs, pp. 65–72. Morgan Kaufmann, San Francisco (1995)Google Scholar
  8. 8.
    Ochoa, G.: Setting the Mutation Rate: Scope and Limitations of the 1/L Heuristic. In: Proc. Genetic and Evolutionary Comp. Conf., Morgan Kaufmann, San Francisco (2002)Google Scholar
  9. 9.
    Rudolph, G.: Self-Adaptive Mutations Lead to Premature Convergence. IEEE Trans. on Evolutionary Computation 5(4), 410–414 (2001)CrossRefGoogle Scholar
  10. 10.
    Smith, J.E., Fogarty, T.C.: Operator and Parameter Adaptation in Genetic Algorithms. Soft Computing, vol. 1, pp. 81–87. Springer, Heidelberg (1997)Google Scholar
  11. 11.
    Srinivas, M., Patnaik, L.M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans. on Systems, Man and Cybernetics 24(4), 656–667 (1994)CrossRefGoogle Scholar
  12. 12.
    Thierens, D.: Adaptive Mutation Control Schemes in Genetic Algorithms. In: Proc. of Congress on Evolutionary Computing, IEEE, Los Alamitos (2002)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sima Uyar
    • 1
  • Sanem Sariel
    • 1
  • Gulsen Eryigit
    • 1
  1. 1.Electrical and Electronics Faculty, Department of Computer EngineeringIstanbul Technical UniversityMaslak, IstanbulTurkey

Personalised recommendations