Advertisement

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)

Abstract

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.

Keywords

Genetic Algorithm Convergence Speed Good Individual Hierarchy Level Quadratic Growth 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: Schaffer, J. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 428–433. Morgan Kaufmann, San Francisco (1989)Google Scholar
  2. 2.
    Alba, E., Tomassini, M.: Parallelism and Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  3. 3.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms, 2nd edn. Book Series on Genetic Algorithms and Evolutionary Computation, vol. 1. Kluwer Academic Publishers, Dordrecht (2000)MATHGoogle Scholar
  4. 4.
    Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley, Chichester (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular evolutionary algorithms. IEEE Trans. on Evolutionary Computation 9(2), 126–142 (2005)CrossRefGoogle Scholar
  6. 6.
    Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Systems, Man and Cybernetics - Part B 35(6), 1272–1282 (2005)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D., Deb, K.: Foundations of Genetic Algorithms. In: A comparative analysis of selection scheme used in genetic algorithms, pp. 69–93. Morgan Kaufmann Publishers, San Francisco (1991)Google Scholar
  8. 8.
    Giacobini, M., Tomassini, M., Tettamanzi, A., Alba, E.: Synchronous and asynchronous cellular evolutionary algorithms for regular lattices. IEEE Transactions on Evolutionary Computation 9(5), 489–505 (2005)CrossRefGoogle Scholar
  9. 9.
    Schaffer, J., Eshelman, L.: On crossover as an evolutionary viable strategy. In: 4th ICGA, pp. 61–68. Morgan Kaufmann, San Francisco (1991)Google Scholar
  10. 10.
    Goldberg, D., Deb, K., Horn, J.: Massively multimodality, deception and genetic algorithms. In: Proc. of the PPSN-2, pp. 37–46. North-Holland, Amsterdam (1992)Google Scholar
  11. 11.
    Jong, K.D., Potter, M., Spears, W.: Using problem generators to explore the effects of epistasis. In: 7th ICGA, pp. 338–345. Morgan Kaufmann, San Francisco (1997)Google Scholar
  12. 12.
    Stinson, D.: An Introduction to the Design and Analysis of Algorithms. The Charles Babbage Research Center, Winnipeg, Manitoba, Canada (1985) (2nd edn., 1987)Google Scholar
  13. 13.
    S.K., Bäck, T., Heitkötter, J.: An evolutionary approach to combinatorial optimization problems. In: Proceedings of the 22nd annual ACM computer science conference (CSC 1994), pp. 66–73. ACM Press, New York (1994)Google Scholar

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

Personalised recommendations