Hierarchical Cellular Genetic Algorithm

  • Stefan Janson
  • Enrique Alba
  • Bernabé Dorronsoro
  • Martin Middendorf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)


Cellular Genetic Algorithms (cGA) are spatially distributed Genetic Algorithms that, because of their high level of diversity, are superior to regular GAs on several optimization functions. Also, since these distributed algorithms only require communication between few closely arranged individuals, they are very suitable for a parallel implementation. We propose a new kind of cGA, called hierarchical cGA (H-cGA), where the population structure is augmented with a hierarchy according to the current fitness of the individuals. Better individuals are moved towards the center of the grid, so that high quality solutions are exploited quickly, while at the same time new solutions are provided by individuals at the outside that keep exploring the search space. This algorithmic variant is expected to increase the convergence speed of the cGA algorithm and maintain the diversity given by the distributed layout. We examine the effect of the introduced hierarchy by observing the variable takeover rates at different hierarchy levels and we compare the H-cGA to the cGA algorithm on a set of benchmark problems and show that the new approach performs promising.


Genetic Algorithm Convergence Speed Good Individual Hierarchy Level Quadratic Growth 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Janson
    • 1
  • Enrique Alba
    • 2
  • Bernabé Dorronsoro
    • 2
  • Martin Middendorf
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigGermany
  2. 2.Department of Computer ScienceUniversity of MálagaSpain

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