Skip to main content
SpringerLink
Log in

Parallel Genetic Algorithms in Optimization

  • Conference paper
Physik und Informatik — Informatik und Physik

Part of the book series: Informatik-Fachberichte ((INFORMATIK,volume 306))

Abstract

Random search methods based on evolutionary principles have been proposed in the 60’s. They did not have a major influence on mainstream optimization. We believe that this will change. The unique power of evolutionary algorithms shows up with parallel computers. Firstly, our parallel genetic algorithm PGA introduced in 1987 [MGSK87] runs especially efficient on parallel computers. Secondly, our research indicates that parallel searches with information exchange between the searches are often better than independent searches. Thus the PGA is a truly parallel algorithm which combines the hardware speed of parallel processors and the software speed of intelligent parallel searching.

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 49.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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. R. K. Belew and L. Booker, editors. Procedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, 1991.

    Google Scholar 

  2. G.F.M. Beenker, T.A.C.M. Claasen, and P.W.C Hermens. Binary sequences with a maximally flat amplitude spectrum. Phill J. of Researc., 40: 289, 1985.

    MATH  Google Scholar 

  3. J.P. Cohoon, S.U. Hedge, W.N. Martin, and D. Richards. Punctuated equilibria: A parallel genetic algorithm. In J.J. Grefenstette, editor, Proceedings of the Second International Conference on Genetic Algorithm., pages 148–154. Lawrence Erlbaum, 1987.

    Google Scholar 

  4. C. de Groot, D. Würtz, and K.H. Hoffmann. Low autocorrelation binary sequences: exact enumeration and optimization by evolutionary strategies. Technical report, ETH Zurich IPS 89–09, 1989.

    Google Scholar 

  5. R. Durbin, R. Szelinski, and A.L. Yuille. An analysis of the elastic net approach to the travelling salesman problem. Neural Comp., 1: 348–358, 1989.

    Article  Google Scholar 

  6. G.C. Everstine. A comparison of three resquencing algorithms for the reduction of matrix profile and wavefront. Int. J. Numer. Meth. in Engin., 14: 837–853, 1979.

    Article  MATH  Google Scholar 

  7. M.J.E. Golay and D. Harris. A new search for skewsymmetric binary sequences with optimal merit factors. Technical report, 1989.

    Google Scholar 

  8. F. Glover. Heuristics for integer programming using surrogate constraints. Decision Science., 8: 156–166, 1977.

    Article  Google Scholar 

  9. F. Glover. Tabu search for nonlinear and parametric optimization. Technical report, University of Boulder, 1991.

    Google Scholar 

  10. M.J.E. Golay. Sieves for low autocorrelation binary sequences. IEEE Trans. on Inform Theor., 23: 43, 1977.

    Article  MATH  Google Scholar 

  11. M.J.E. Golay. The merit factor of long low autocorrelation binary sequences. IEEE Trans. on Inform. Syst., 28: 543, 1982.

    Article  Google Scholar 

  12. D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.

    MATH  Google Scholar 

  13. M. Gorges-Schleuter. Asparagos: An asynchronous parallel genetic optimization strategy. In H. Schaffer, editor, 3rd Int. Conf. on Genetic Algorithm., pages 422–427. Morgan- Kaufmann, 1989.

    Google Scholar 

  14. M. Gorges-Schleuter. Genetic Algorithms and Population Structures - A Massively Parallel Algorithm. PhD thesis, University of Dortmund, 1991.

    Google Scholar 

  15. J.R. Gilbert and E. Zmijewski. A parallel graph partitioning algorithm for message- passing multiprocessors. In First Int. Conf. on Supercomputin., 1987.

    Google Scholar 

  16. J.H. Holland. Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  17. D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning. Operations Researc., 37: 865–892, 1989.

    Article  MATH  Google Scholar 

  18. D.S. Johnson. Local Optimization and the Traveling Salesman Problem. In M.S. Paterson, editor, Automata, Languages and Programmin., Lecture Notes in Computer Science 496, pages 446–461. Springer-Verlag, 1990.

    Chapter  Google Scholar 

  19. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Scienc., 220: 671–680, 1983.

    Article  MathSciNet  MATH  Google Scholar 

  20. B.W. Kernighan and S.Lin. An efficient heuristic procedure for partitioning graphs. Bell Syst. Techn Journ., 2: 291–307, 1970.

    Article  Google Scholar 

  21. S. Kirkpatrick and G. Toulouse. Configuration space analysis of travelling salesman problems. J.Physiqu., 46: 1277–1292, 1985.

    Article  MathSciNet  Google Scholar 

  22. S. Lin. Computer solutions of the traveling salesman problem. Bell. Syst. Techn. Journ., 44: 2245–2269, 1965.

    Article  MATH  Google Scholar 

  23. S. Lin and B. W. Kernighan. An efficient heuristic for the traveling salesman problem. Operations Researc., 21: 298–516, 1973.

    Article  MathSciNet  Google Scholar 

  24. H. Mühlenbein, M. Gorges-Schleuter, and O. Krämer. New solutions to the mapping problem of parallel systems - the evolution approach. Parallel Computin., 6: 269–279, 1987.

    Article  Google Scholar 

  25. H. Mühlenbein, M. Gorges-Schleuter, and O. Krämer. Evolution algorithms in combinatorial optimization. Parallel Computin., 7: 65–88, 1988.

    Article  MATH  Google Scholar 

  26. D. Moore. A round-robin parallel partitioning algorithm. Technical Report 88–916, Cornell University, 1988.

    Google Scholar 

  27. B. Manderick and P. Spiessens. Fine-grained parallel genetic algorithm. In H. Schaffer, editor, 3rd Int. Conf. on Genetic Algorithm., pages 428–433. Morgan-Kaufmann, 1989.

    Google Scholar 

  28. H. Mühlenbein, M. Schomisch, and J. Born. The parallel genetic algorithm as function optimizer. Parallel Computin., 17: 619–632, 1991.

    Article  MATH  Google Scholar 

  29. H. Mühlenbein. Parallel genetic algorithm, population dynamics and combinatorial optimization. In H. Schaffer, editor, 3rd Int. Conf. on Genetic Algorithm., pages 416–421. Morgan Kaufmann, 1989.

    Google Scholar 

  30. H. Mühlenbein. Darwin’s continent cycle theory and its simulation by the prisoner’s dilemma. Complex System., 1991.

    Google Scholar 

  31. H. Mühlenbein. Evolution in time and space - the parallel genetic algorithm. In G. Rawlins, editor, Foundations of Genetic Algorithm., pages 316–337, San Mateo, 1991. Morgan- Kaufman.

    Google Scholar 

  32. C. Peterson. Parallel Distributed Approaches to Combinatorial Optimization: Benchmark Studies on Traveling Salesman Problem. Neural Computatio., 2: 261–269, 1990.

    Article  Google Scholar 

  33. W. Padberg and G. Rinaldi. Optimization of a 532—city symmetric traveling saleman problem by branch and cut. Op. Res. Let., 6:1–7, 1987.

    Article  MathSciNet  MATH  Google Scholar 

  34. C. Peterson and B. Söderberg. A new method for mapping optimization problems onto neural networks. Int. J. Neural Syst., 1: 995–1019, 1989.

    Article  Google Scholar 

  35. I. Rechenberg. Evolutionsstrategi. - Optimierung technischer Systeme nach Prinzipien der biologischen Information. Fromman Verlag, Freiburg, 1973.

    Google Scholar 

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

    MATH  Google Scholar 

  37. H. Schaffer, editor. Procedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, 1989.

    Google Scholar 

  38. R. Tanese. Distributed genetic algorithm. In H. Schaffer, editor, 3rd Int. Conf. on Genetic Algorithm., pages 434–440. Morgan-Kaufmann, 1989.

    Google Scholar 

  39. N.L.J. Ulder, E. Pesch, P.J.M. van Laarhoven, H.-J. Bandelt, and E.H.L. Aarts. Genetic Local Search Algorithm for the Traveling Salesman Problem. In R. Maenner and H.- P. Schwefel, editors, Parallel Problem Solving from Natur., Lecture Notes in Computer Science 496, pages 109–116. Springer-Verlag, 1991.

    Chapter  Google Scholar 

  40. G. von Laszewski. Ein paralleler genetischer Algorithmus für das Graph Partitionier ungsproblem. Master’s thesis, Universität Bonn, 1990.

    Google Scholar 

  41. G. von Laszewski. Intelligent Structural Operators for the k-way Graph Partitioning problem. In R.K. Belew and L. Booker, editors, Procedings of the Fourth International Conference on Genetic Algorithm., pages 45–52. Morgan Kaufman, 1991.

    Google Scholar 

  42. Q. Wang. Optimization by simulating molecular evolution. Biol. Cybern., 57: 95, 1987.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Gesellschaft für Mathematik und Datenverarbeitung, D-5205, Sankt Augustin 1, Deutschland

    H. Mühlenbein

Authors
  1. H. Mühlenbein
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dornier GmbH, Postfach 1420, W-7990, Friedrichshafen 1, Deutschland

    D. Krönig

  2. Lehrstuhl für Elektroakustik, Technische Universität München, Arcisstr. 21, W-8000, München 2, Deutschland

    M. Lang

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mühlenbein, H. (1992). Parallel Genetic Algorithms in Optimization. In: Krönig, D., Lang, M. (eds) Physik und Informatik — Informatik und Physik. Informatik-Fachberichte, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77382-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-77382-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55298-7

  • Online ISBN: 978-3-642-77382-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation