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A Dynamic Migration Model for Self-adaptive Genetic Algorithms

  • K. G. Srinivasa
  • K. Sridharan
  • P. Deepa Shenoy
  • K. R. Venugopal
  • Lalit M. Patnaik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3578)

Abstract

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Keywords

Genetic Algorithm Search Space Mutation Rate Crossover Point Good Individual 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • K. G. Srinivasa
    • 1
  • K. Sridharan
    • 2
  • P. Deepa Shenoy
    • 1
  • K. R. Venugopal
    • 1
  • Lalit M. Patnaik
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of EngineeringBangalore
  2. 2.Department of CSESunny Buffalo 
  3. 3.Microprocessor Applications LaboratoryIndian Institute of ScienceIndia

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