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.


Genetic Algorithm Mutation Rate Average Fitness Adaptive Mutation Convergence Control 
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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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

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