Supporting polyploidy in genetic algorithms using dominance vectors

  • Ben S. Hadad
  • Christoph F. Eick
Enhanced Evolutionary Operators
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)


By memorizing alleles that have been successful in the past, polyploidy has been found to be beneficial for adapting to changing environments. This paper explores the benefits of using polyploidy in Genetic Algorithms. Polyploidy is provided in our approach by using a local chromosome to reflect dominance in diploid and tetraploid organisms, with and without evolving crossover points, added to provide linkage between chromosomes and the dominance control vector. We compare our polyploid approach to a haploid implementation for a benchmark that involves a 0/1 knapsack problem with time varying weight constraints.


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  1. [1]
    Bagley, J. D., The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms. (Doctoral dissertation, University of Michigan). 1967, Dissertation Abstracts International 28(12), 5106B. (University Microfilms No. 68-7556).Google Scholar
  2. [2]
    Brindle, A., Genetic Algorithms for Function Optimization. Unpublished doctoral dissertation, University of Alberta, Edmonton.Google Scholar
  3. [3]
    Eschelman, L. J., Caruana, R.A., and Schaffer, J.D., Biases in the Crossover Landscape, Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 1989.Google Scholar
  4. [4]
    Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, MA, Addison Wesley, 1989.Google Scholar
  5. [5]
    Goldberg, D.E., & Rudnick, M. W., “Genetic algorithms and the variance of fitness,” Complex Systems, 5(3). 265–278.Google Scholar
  6. [6]
    Holland, J. H., Adaptation in natural and artificial systems. Ann Arbor: The University of Michigan Press, 1975.Google Scholar
  7. [7]
    Holstien, R. B. Artificial Genetic Adaptation in Computer Control Systems. (Doctoral dissertation, University of Michigan). Dissertation Abstracts International, 32(3), 1510B. (University Microfilms No. 71-23773).Google Scholar
  8. [8]
    Michalewicz, Zbigniew, Genetic Algorithms + Data Structures = Evolution Programs, Berlin, Springer-Verlag, 1994.Google Scholar
  9. [9]
    Ng, K. P., & Wong, K. C., A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization, Proceedings of the Sixth International Conference. on Genetic Algorithms, CA, Morgan Kaufmann, 1995.Google Scholar
  10. [10]
    Smith, R.E., & Goldberg, D.E., Diploidy and Dominance in Artificial Genetic Search, Complex Systems 6(1992), 251–285.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Ben S. Hadad
    • 1
  • Christoph F. Eick
    • 1
  1. 1.Department of Computer ScienceUniversity of HoustonHouston

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