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Selection schemes with spatial isolation for genetic optimization

  • Károly F. Pál
Modification and Extensions to Evolutionary Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

We tested genetic algorithms with several selection schemes on a massively multimodal spin-lattice problem. New schemes that introduce a spatial separation between the members of the population gave significantly better results than any other scheme considered. These schemes slow down considerably the flow of genetic information between different regions of the population, which makes possible for distant regions to evolve more or less independently. This way many distinct possibilities can be explored simultaneously and a high degree of diversity can be maintained, which is very important for most multimodal problems.

Keywords

Genetic Algorithm Selection Scheme Hybrid Algorithm Mating Pool Uniform Crossover 
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

  1. 1.
    K. F. Pál: Genetic algorithms with local optimization. Submitted to Biol. Cybern.Google Scholar
  2. 2.
    F. Montoya, J.-M. Dubois: Darwinian adaptive simulated annealing. Europhys. Lett. 22 (1993) 79–84.Google Scholar
  3. 3.
    J. H. Holland: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, 1975.Google Scholar
  4. 4.
    D. E. Goldberg: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, 1989.Google Scholar
  5. 5.
    D. E. Goldberg: Genetic algorithms and Walsh functions: part II, deception and its analysis. Complex Systems 3 (1989) 153–171.MathSciNetGoogle Scholar
  6. 6.
    A. Brindle: Genetic algorithms for function optimization. Unpublished doctoral dissertation, University of Alberta, Edmonton, 1981.Google Scholar
  7. 7.
    K. F. Pál: Genetic algorithms for the traveling salesman problem based on a heuristic crossover operation. Biol Cybern 69 (1993) 539–546.CrossRefGoogle Scholar
  8. 8.
    D. Whitley: The Genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: J. D. Schaffer (ed.): Proceedings of the third international conference on genetic algorithms. George Mason University 1989, pp. 116–121.Google Scholar
  9. 9.
    S. W. Mahfoud: Crowding and preselection revisited. Parallel Problem Solving from Nature 2 (1992) 27–36.Google Scholar
  10. 10.
    D.E. Goldberg, K. Deb: A comparative analysis of selection schemes used in genetic algorithms. In: G. J. E. Rawlins (ed.): Foundations of genetic algorithms. San Mateo, California: Morgan Kaufmann Publishers 1991, pp. 69–93.Google Scholar
  11. 11.
    G. Syswerda: Uniform crossover in genetic algorithms. In: J. D. Schaffer (ed.): Proceedings of the third international conference on genetic algorithms. George Mason University 1989, pp. 2–9.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Károly F. Pál
    • 1
  1. 1.Institute of Nuclear Research of the Hungarian Academy of SciencesDebrecenHungary

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