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Migration through Mutation Space: A Means of Accelerating Convergence in Evolutionary Algorithms

  • H. Copland
  • T. Hendtlass
Conference paper

Abstract

In this paper a multiple subpopulations technique for evolutionary algorithms is proposed. Each subpopulation is distinguished by the mutation radius and mutation probability assigned to it, with mutation radius being a function of mutation probability. Mutation probabilities across the subpopulations range from 0.005 to 0.75. There is no crossover between subpopulations in the normal course of breeding, and the mechanisms of elite migration from a higher mutation subpopulation to the adjacent subpopulation of lower mutation is used to introduce new genetic material. The evolution of artificial neural networks for solving a variety of problems is demonstrated, with convergence times typically half as long as a standard evolutionary algorithm.

Keywords

Evolutionary Algorithm Mutation Probability Lower Mutation Parallel Genetic Algorithm Mutation Environment 
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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    R. Tanese. Distributed genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms, pages 434–439, George Mason University, June 4th-7th 1989.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • H. Copland
    • 1
  • T. Hendtlass
    • 1
  1. 1.Centre for Intelligent SystemsSwinburne University of TechnologyHawthornAustralia

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