Skip to main content

Parallel genetic algorithms, population genetics and combinatorial optimization

Part of the Lecture Notes in Computer Science book series (LNAI,volume 565)

Abstract

In this paper we introduce our asynchronous parallel genetic algorithm ASPARAGOS. The two major extensions compared to genetic algorithms are the following. First, individuals live on a 2-D grid and selection is done locally in the neighborhood. Second, each individual does local hill climbing. The rationale for these extensions is discussed within the framework of population genetics. We have applied ASPARAGOS to an important combinatorial optimization problem, the quadratic assignment problem. ASPARAGOS found a new optimum for the largest published problem. It is able to solve much larger problems. The algorithm uses a polysexual voting recombination operator.

Keywords

  • Genetic Algorithm
  • Travel Salesman Problem
  • Travel Salesman Problem
  • Quadratic Assignment Problem
  • Island Model

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.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/3-540-55027-5_23
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-46663-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackley D. H. An Empirical Study of Bit Vector Function Optimization. in Dav87, 87.

    Google Scholar 

  2. Axelrod R. The Evolution of strategies in the Iterated Prisoner's Dilemma. in Dav87, 87.

    Google Scholar 

  3. Burkard R. E., Rendl F. A thermodynamically motivated simulation procedure for combinatorial optimization problems. Europ. Journ. of Operat. Research, 17:169–174, 84.

    Google Scholar 

  4. Burkard R. Quadratic Assignment Problems. Europ. Journal of Operations Research, 15:283–289, 84.

    Google Scholar 

  5. Cohoon D. P. et al. Punctuated Equilibria: A Parallel Genetic Algorithm. in Gre87, 87.

    Google Scholar 

  6. Crow J. E. Basic Concepts in Population, Quantitative, and Evolutionary Genetics. Freeman, New York, 86.

    Google Scholar 

  7. Darwin C. The Origin of Species by Means of Natural. Penguin Books, London, 1859.

    Google Scholar 

  8. Davis L. Genetic Algorithms and Simulated Annealing. Morgan Kaufmann, Los Altos, 87.

    Google Scholar 

  9. De Jong K. Adaptive system design: A genetic approach. IEEE Trans. Syst., Man and Cybern., 10:566–574, 80.

    Google Scholar 

  10. Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Adison-Wesley, 89.

    Google Scholar 

  11. Gorges-Schleuter M. ASPARAGOS: Simulation of the TSP. to be published, 89.

    Google Scholar 

  12. Grefenstette J. J., editor. Genetic Algorithms and their Applications, Hillsdale Lawrence Erlbaum Ass., Proc. 2nd Conf. on Genetic Algorithms, 87.

    Google Scholar 

  13. Holland J. H. Adaptation in natural and artificial systems. Ann Arbor, University of Michigan Press, 75.

    Google Scholar 

  14. Lin S. Computer solution of the traveling salesman problem. Bell. Sys, Tech. Journ., 44:2245–2269, 65.

    Google Scholar 

  15. Mühlenbein H., Gorges-Schleuter M., Krämer O. Evolution Algorithms in Combinatorial Optimization. Parallel Computing, 7(1):65–88, 88.

    Google Scholar 

  16. Mühlenbein H., Kindermann J. The Dynamics of Evolution and Learning — Towards Genetic Neural Networks. in: Connectionism in Perspectives, J. Pfeiffer ed., 89

    Google Scholar 

  17. Mühlenbein H., Krämer O., Peise G., Rinn R. The MEGAFRAME HYPERCLUSTER — A Reconfigurable Architecture for Massively Parallel Computers. 89. to be published.

    Google Scholar 

  18. Maynard Smith J. When learning guides evolution. Nature, 329:761–762, 87.

    Google Scholar 

  19. Napierala G. Ein paralleler Ansatz zur Lösung des TSP. Diplomarbeit, University Bonn, Jan 89.

    Google Scholar 

  20. Nature. Scientific correspondence. Nature 336, 527–528, 88.

    Google Scholar 

  21. Pettey C. B. et al. A parallel genetic algorithm. in Gre87, 87.

    Google Scholar 

  22. Spiessens P. Genetic Algorithms. AI-MEMO 88-19, VUB Brussels, 88.

    Google Scholar 

  23. Sheraldi H. D., Rajgopal P. A Flexible, Polynomialtime Construction and Improvement Heuristic for the Quadratic Assignment Problem. Operations Research, 13:587–600, 86.

    Google Scholar 

  24. Tanese R. Parallel Genetic Algorithms for the Hypercube. in Gre87, 87.

    Google Scholar 

  25. Wright S. The Roles of Mutation, Inbreeding, Crossbreeding and Selection in Evolution. In Proc. 6th Int. Congr. Genetics, pages 356–366, 32.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mühlenbein, H. (1991). Parallel genetic algorithms, population genetics and combinatorial optimization. In: Becker, J.D., Eisele, I., Mündemann, F.W. (eds) Parallelism, Learning, Evolution. WOPPLOT 1989. Lecture Notes in Computer Science, vol 565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55027-5_23

Download citation

  • DOI: https://doi.org/10.1007/3-540-55027-5_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55027-3

  • Online ISBN: 978-3-540-46663-5

  • eBook Packages: Springer Book Archive