A comparative study of global and local selection in evolution strategies

  • Martina Gorges-Schleuter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


Traditionally, selection in Evolutionary Algorithms operates global on the entire population. In nature we rarely find global mating pools and thus we introduce a more-or-less geographical isolation in which individuals may interact only with individuals in the immediate locality, the local overlapping neighborhoods.

This paper studies two classes of diffusion models for Evolution Strategies (ES) where the decision for survival as well as the parent choice is performed locally only. The classes differ in that we either allow both parents to be chosen randomly from the neighborhood or one parent is chosen to be the centre individual and the other one is chosen randomly from the neighborhood. We introduce a notation for the diffusion model ES, give a theoretical analysis and present results of a numerical study.


Population Structure Evolution Strategy Parent Selection Local Selection Fractal Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    I. Rechenberg, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Information, Frommann Verlag, Stuttgart (1973) in germanGoogle Scholar
  2. 2.
    H.-P. Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons (1981), english translation of the original edition by Birkhäuser, Basel (1977)Google Scholar
  3. 3.
    J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor (1975)Google Scholar
  4. 4.
    B. Manderick, P. Spiessens, Fine-grained Parallel Genetic Algorithm, in Proc. of the 3rd Int. Conf. on Genetic Algorithms, Morgan Kaufmann (1989) 428–433Google Scholar
  5. 5.
    R. Collins, D. Jefferson, Selection in Massively Parallel Genetic Algorithms, in Proc. 4th Int. Conf. on Genetic Algorithms, Morgan Kaufmann (1991) 249–256Google Scholar
  6. 6.
    M. Gorges-Schleuter, ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy, in Proc. of the 3rd ICGA, Morgan Kaufmann (1989) 422–427Google Scholar
  7. 7.
    M. Gorges-Schleuter, Explicit Parallelism of GAs through Population Structures, Proc. of PPSN I, LNCS 496, Springer Verlag (1991) 150–159Google Scholar
  8. 8.
    M. Gorges-Schleuter, Genetic Algorithms and Population Structures, Doctoral dissertation, University of Dortmund (1991). Extended abstract in V. Plantamura et al (Eds.), Frontier Decision Support Concepts, Wiley, New York (1994) 261–319Google Scholar
  9. 9.
    J. Sprave, Linear neighborhood Evolution Strategy, Conf. on Evolutionary Programming (1994); Software Package LICE-1.02, both via Scholar
  10. 10.
    H.-P. Schwefel, G. Rudolph, Contemporary Evolution Strategies, Third Int. Conf. on Artificial Life, LNCS 929, Springer Verlag, Berlin (1995) 893–907Google Scholar
  11. 11.
    D. Goldberg, K. Deb, A comparative analysis of selection schemes used in genetic algorithms, in Foundations of Genetic Algorithms, Morgan Kaufmann (1991) 69–93Google Scholar
  12. 12.
    M. Gorges-Schleuter, On Global and Local Selection in Evolution Strategies, submitted to FOGA 5, Leiden (1998)Google Scholar
  13. 13.
    Th. Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York (1995)Google Scholar
  14. 14.
    Th. Bäck, GENEsYs 1.0, Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Martina Gorges-Schleuter
    • 1
  1. 1.Institute for Applied Computer ScienceForschungszentrum KarlsruheKarlsruheGermany

Personalised recommendations