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International Conference on Parallel Problem Solving from Nature

PPSN 1994: Parallel Problem Solving from Nature — PPSN III pp 418–427Cite as

Parallel optimization of evolutionary algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 866))

Abstract

A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization).

The genetic algorithms which result from the meta-evolution experiment are considerably faster than standard genetic algorithms and confirm recent theoretical results about optimal mutation rates and the interaction of selective pressure and mutation rate.

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References

  1. Th. Bäck. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In Männer and Manderick [14], pages 85–94.

    Google Scholar 

  2. Th. Bäck. Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In Proceedings of the IEEE International Conference on Evolutionary Computation, 1994.

    Google Scholar 

  3. Th. Bäck and F. Hoffmeister. Extended selection mechanisms in genetic algorithms. In Belew and Booker [6], pages 92–99.

    Google Scholar 

  4. Th. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1–23, 1993.

    Google Scholar 

  5. J. E. Baker. Adaptive selection methods for genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the 1st International Conference on Genetic Algorithms and Their Applications, pages 101–111. Lawrence Erlbaum Associates, Hillsdale, NJ, 1985.

    Google Scholar 

  6. R. K. Belew and L. B. Booker, editors. Proceedings of the 4th International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, San Mateo, CA, 1991.

    Google Scholar 

  7. H. J. Bremermann. The evolution of intelligence: The nervous system as a model of its environment. Technical Report 1, Contract No. 477(17), Department of Mathematics, University of Washington, Seattle, July 1958.

    Google Scholar 

  8. B. Freisleben and M. Härtfelder. Optimization of genetic algorithms by genetic algorithms. In R. F. Albrecht, C. R. Reeves, and N. C. Steele, editors, Artificial Neural Nets and Genetic Algorithms, pages 392–399. Springer, Wien, 1993.

    Google Scholar 

  9. D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, MA, 1989.

    Google Scholar 

  10. D. E. Goldberg and K. Deb. A comparative analysis of selection schemes used in genetic algorithms. In Rawlins [16], pages 69–93.

    Google Scholar 

  11. J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-16(1):122–128, 1986.

    Google Scholar 

  12. W. E. Hart and R. K. Belew. Optimizing an arbitrary function is hard for the genetic algorithm. In Belew and Booker [6], pages 190–195.

    Google Scholar 

  13. K. A. De Jong. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975. Diss. Abstr. Int. 36(10), 5140B, University Microfilms No. 76–9381.

    Google Scholar 

  14. R. Männer and B. Manderick, editors. Parallel Problem Solving from Nature 2. Elsevier, Amsterdam, 1992.

    Google Scholar 

  15. H. Mühlenbein. How genetic algorithms really work: I. mutation and hillclimbing. In Männer and Manderick [14], pages 15–25.

    Google Scholar 

  16. G. J. E. Rawlins, editor. Foundations of Genetic Algorithms. Morgan Kaufmann Publishers, San Mateo, CA, 1991.

    Google Scholar 

  17. J. D. Schaffer, editor. Proceedings of the 3rd International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, San Mateo, CA, 1989.

    Google Scholar 

  18. J. D. Schaffer, R. A. Caruana, L. J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In Schaffer [17], pages 51–60.

    Google Scholar 

  19. J. D. Schaffer and L. J. Eshelman. On crossover as an evolutionary viable strategy. In Belew and Booker [6], pages 51–60.

    Google Scholar 

  20. H.-P. Schwefel. Numerical Optimization of Computer Models. Wiley, Chichester, 1981.

    Google Scholar 

  21. G. Syswerda. Uniform crossover in genetic algorithms. In Schaffer [17], pages 2–9.

    Google Scholar 

  22. A. H. Wright. Genetic algorithms for real parameter optimization. In Rawlins [16], pages 205–218.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Chair XI, University of Dortmund, D-44221, Dortmund, Germany

    Thomas Bäck

Authors
  1. Thomas Bäck
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Editor information

Yuval Davidor Hans-Paul Schwefel Reinhard Männer

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© 1994 Springer-Verlag Berlin Heidelberg

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Bäck, T. (1994). Parallel optimization of evolutionary algorithms. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_285

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  • DOI: https://doi.org/10.1007/3-540-58484-6_285

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58484-1

  • Online ISBN: 978-3-540-49001-2

  • eBook Packages: Springer Book Archive

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