Skip to main content
SpringerLink
Log in

Scalable Parallelism by Evolutionary Algorithms

  • Conference paper
Parallel Computing and Mathematical Optimization

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 367))

Abstract

Parallel computers are widely available for several years. They are the only means to escape from physical limitations which restrict the maximum performance of von-Neumann computers. According to Flynn’s classification [Fly66] parallel computers basically separate into SIMD and MIMD machines. Vector processors and array computers are typical members of the former class, while multi-processors with shared or distributed memory represent the latter class.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G. Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. In AFIPS Conference Proceedings, volume 30, pages 483–485, Washington, D.C., April 1967. Thompson Books.

    Google Scholar 

  2. James Edward Baker. Adaptive selection methods for genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the first international conference on genetic algorithms and their applications, pages 101–111, Hillsdale, New Jersey, 1985. Lawrence Erlhaum Associates.

    Google Scholar 

  3. U. Bernutat-Buchmann and J. Krieger. Evolution strategies in numerical optimization on vector computers. In Feilmeier, Joubert, and Schendel, editors. Parallel Computing 83, Proceedings of the International Conference on Parallel Computing, pages 99–105, FU Berlin, 26.-28.Sept. 1983.

    Google Scholar 

  4. Joachim Born. Evolutionsstrategien zur numerischen Lösung von Adaptationsaufgaben. Dissertation A, Humboldt-Universität, Berlin, GDR, 1978.

    Google Scholar 

  5. Andreas Bormann. Parallelisierungsmöglichkeiten für direkte Optimierungsverfahren auf Transputersystemen. Master thesis, University of Dortmund, Dortmund, Germany, April 1989.

    Google Scholar 

  6. Richard A. Caruna, Larry J. Eshelman, and J. David Schaffer. Representation and hidden bias II: Eliminating defining length bias in genetic search via shuffle crossover. In N. S. Sridharan, editor, Eleventh international joint conference on artificial intelligence, pages 750–755. Morgan Kaufmann Publishers, August 1989.

    Google Scholar 

  7. J.P. Cohoon, S.U. Hedge, W.N. Martin, and D. Richards. Punctuated equilibria: A parallel genetic algorithm. In J.J. Grefenstette, editor, Genetic Algorithms and their Applications: Proc. of the Second Int.Conf. on Genetic Algorithms, pages 148–154, Hillsdale, New Jersey, July 1987. Lawrence Erlbaum Associates.

    Google Scholar 

  8. Larry L. Eshelman, Richard A. Caruna, and J. David Schaffer. Biases in the crossover landscape. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 10–19. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  9. M.J. Flynn. Very high-speed computing systems. Proceedings of the IEEE, 54(12):1901–1909, December 1966.

    Article  Google Scholar 

  10. John J. Grefenstette and James E. Baker. How genetic algorithms work: A critical look at implicit parallelism. In J. David Schaffer, editor. Proceedings of the third international conference on genetic algorithms and their applications, pages 20–27. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  11. David E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.

    Google Scholar 

  12. David E. Goldberg. Sizing populations for serial and parallel genetic algorithms. In J. David Schaffer, editor. Proceedings of the third international conference on genetic algorithms and their applications, pages 70–79. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  13. Martina Gorges-Schleuter. Asparagos: an asynchronous parallel genetic optimization strategy. In J. David Schaffer, editor. Proceedings of the Third International Conference on Genetic Algorithms and their Applications, pages 422–427. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  14. Frank Hoffmeister and Thomas Bäck. Genetic algorithms and evolution strategies: Similarities and differences. Technical Report „Grüne Reihe“ No. 365, Department of Computer Science, University of Dortmund, November 1990

    Google Scholar 

  15. John H. Holland. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  16. Kenneth 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 

  17. Patrick Krolak, Wayne Felts, and George Marble. A man-machine approach towards solving the traveling salesman problem. Communications of the ACM, 14(5):327–334, 1971.

    Article  Google Scholar 

  18. R. Kottkamp. Nicht-lineare Optimierung unter Verwendung verteilter, paralleler Prozesse in einem Local Area Network (LAN). Master thesis, University of Dortmund, Dortmund, FRG, February 1989.

    Google Scholar 

  19. Reinhard Männer and Hans-Paul Schwefel, editors. Proceedings of the First International Conference on Parallel Problem Solving from Nature (PPSN), Dortmund, Germany, 1990, Berlin, 1991. Springer, (in print).

    Google Scholar 

  20. J.A. Nelder and R. Mead. A simplex method for function minimization. Computer Journal, 7:308–313, 1965.

    Google Scholar 

  21. Ingo Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Dissertation, Technical University of Berlin, Berlin, Germany, 1971.

    Google Scholar 

  22. Ingo Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart, 1973.

    Google Scholar 

  23. Günter Rudolph. Globale Optimierung mit Parallelen Evolutionsstrategien. Diploma thesis, University of Dortmund, Department of Computer Science, Dortmund, Germany, July 1990.

    Google Scholar 

  24. Günter Rudolph. Global optimization by means of evolution strategies. In Männer and Schwefel [MS91], pages 209–213. (in print).

    Google Scholar 

  25. J. David Schaffer, Richard A. Caruna, Larry J. Eshelman, and Rajarshi Das. A study of control parameters affecting online performance of genetic algorithm for function optimization. In J. David Schaffer, editor. Proceedings of the third international conference on genetic algorithms and their applications, pages 51–60. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  26. Hans-Paul Schwefel. Numerische Optimierung von Computer-Modellen mittels der Evolutionssirategie. Interdisciplinary systems research; 26. Birkhäuser, Basel, 1977.

    Google Scholar 

  27. Hans-Paul Schwefel. Numerical Optimization of Computer Models. Wiley, Chichester, 1981.

    Google Scholar 

  28. Hans-Paul Schwefel. Optimum seeking methods: Subroutines for the minimization of nonlinear functions of several variables by means of direct (derivative-free) methods. Interner Bericht KFA-STE-IB-7/81, Kernforschungsanlage Jülich GmbH, Jülich, Germany, October 1981.

    Google Scholar 

  29. Hans-Paul Schwefel. Collective phenomena in evolutionary systems. In 1st Annual Meeting of the International Society for General System Research, Budapest, pages 1025–1033, June 1987.

    Google Scholar 

  30. P. Spiessens and B. Manderick. Fine-grained parallel genetic algorithms. In D.J. Schaffer, editor, Proc. of the Third Int.Conf on Genetic Algorithms, pages 428–433, San Mateo, California, June 1989. Morgan Kaufmann.

    Google Scholar 

  31. T. Starkweather, D. Whitley, and K. Mathias. Optimization using distributed genetic algorithms[T1]. In Männer and Schwefel [MS91], pages 176–186. (in print).

    Google Scholar 

  32. Gilbert Syswerda. Uniform crossover in genetic algorithms. In J. David Schaffer, editor. Proceedings of the third international conference on genetic algorithms and their applications, pages 2–9. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  33. R. Tañese. Distributed genetic algorithms. In D.J. Schaffer, editor, Proc. of the Third Int.Conf on Genetic Algorithms, pages 434–439, San Mateo, California, June 1989. Morgan Kaufmann.

    Google Scholar 

  34. A. Törn and A. Zilinskas. Global Optimization, volume 350 ofLecture Notes in Computer Science. Springer, Berlin, FRG, 1989

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science XI, University of Dortmund, P.O. Box 500 500, D-4600, Dortmund 50, Germany

    Frank Hoffmeister

Authors
  1. Frank Hoffmeister
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Siegen, Hölderlinstr. 3, W-5900, Siegen, Germany

    Manfred Grauer

  2. University of Hamburg, Von-Melle-Park 5, W-2000, Hamburg 13, Germany

    Dieter B. Pressmar

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hoffmeister, F. (1991). Scalable Parallelism by Evolutionary Algorithms. In: Grauer, M., Pressmar, D.B. (eds) Parallel Computing and Mathematical Optimization. Lecture Notes in Economics and Mathematical Systems, vol 367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-95665-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-95665-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54434-0

  • Online ISBN: 978-3-642-95665-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation