Skip to main content
SpringerLink
Log in
Book cover

International Conference on Parallel Problem Solving from Nature

PPSN 1998: Parallel Problem Solving from Nature — PPSN V pp 603–612Cite as

Hybrid distributed real-coded genetic algorithms

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

Abstract

Distributed genetic algorithms keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent from the others. A migration mechanism produces a chromosome exchange between the subpopulations. These algorithms may be categorized as homogeneous or heterogeneous ones when all the subpopulations apply genetic algorithms with the same configuration, or not, respectively.

In this paper, we present the hybrid distributed real-coded genetic algorithms. In these algorithms the connection of homogeneous distributed genetic algorithms, which apply different crossover operators and selective pressure degrees, forms a higher level heterogeneous distributed genetic algorithm. Experimental results show that the proposal consistently outperforms equivalent heterogeneous and homogeneous distributed genetic algorithms.

This research has been partially supported by CICYT TIC96-0778 and DGICYT SAB95-0473.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker J.E.: Adaptive Selection Methods for Genetic Algorithms. Proc. of the First Int. Conf. on Genetic Algorithms and their Applications, J.J. Grefenstette (Ed.) (L. Erlbaum Associates, Hillsdale, MA, 1985) 101–111.

    Google Scholar 

  2. Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithm. Proc. Second Int. Conf. on Genetic Algorithms, J.J. Grefenstette (Ed.) (L. Erlbaum Associates, Hillsdale, MA, 1987) 14–21.

    Google Scholar 

  3. CantÚ-Paz E.: A Survey of Parallel Genetic Algorithms. IlliGAL Report 97003, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, IL (1997).

    Google Scholar 

  4. Cohoon J.P., Martin W.N., Richards D.S.: Genetic Algorithms and Punctuated Equilibria in VLSI. Parallel Problem Solving from Nature 1, H.-P. Schwefel, R. Männer (Eds.) (Berlin, Germany, Springer-Verlag, 1990) 134–144.

    Google Scholar 

  5. De Jong K. A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral Dissertation, University of Michigan (1975).

    Google Scholar 

  6. Griewangk A.O.: Generalized Descent of Global Optimization. JOTA 34 (1981) 11–39.

    Article  Google Scholar 

  7. Gruau F.: The Mixed Genetic Algorithm. Parallel Computing: Trends and Application, G.R. Joubert, D. Trystram, F.J. Peters, D.J. Evans (Eds), Elsevier, 1994.

    Google Scholar 

  8. Herrera F., Lozano M.: Heuristic Crossovers for Real-coded Genetic Algorithms Based on Fuzzy Connectives. 4th International Conference on Parallel Problem Solving from Nature (Springer, Berlin, 1996), 336–345.

    Google Scholar 

  9. Herrera F., Lozano M.: Heterogeneous Distributed Genetic Algorithms Based on the Crossover Operator. Second IEE/IEEE Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997, 203–208.

    Google Scholar 

  10. Herrera F., Lozano M.: Gradual Distributed Genetic Algorithms. Technical Report #DECSAI-97-01-03, Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain (1997).

    Google Scholar 

  11. Herrera F., Lozano M., Verdegay J.L.: Dynamic and Heuristic Fuzzy Connectives-Based Crossover Operators for Controlling the Diversity and Convergence of Real-Coded Genetic Algorithms. Int. Journal of Intelligent 11 (1996) 1013–1041.

    Article  MATH  Google Scholar 

  12. Herrera F., Lozano M., Verdegay J.L.: Fuzzy Connectives Based Crossover Operators to Model Genetic Algorithms Population Diversity. Fuzzy Sets and Systems 92(1) (1997) 21–30.

    Article  Google Scholar 

  13. Herrera F., Lozano M., Verdegay J.L.: Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. Artificial Intelligent Review 12(4) (1998).

    Google Scholar 

  14. Lin S-C., Punch III W.F., Goodman E.D.: Coarse-Grain Genetic Algorithms: Categorization and New Approach. Proc. Sixth IEEE Parallel and Distributed Processing (1994) 28–37.

    Google Scholar 

  15. Maresky J.: On Efficient Communication in Distributed Genetic Algorithms. M.S. Dissertation, Institute of Computer Science, The Hebrew University of Jerusalem (1994).

    Google Scholar 

  16. Mühlenbein H., Schomisch M., Born J.: The Parallel Genetic Algorithm as Function Optimizer. Fourth Int. Conf. on Genetic Algorithms, R. Belew, L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo, 1991) 271–278.

    Google Scholar 

  17. Potts J.C., Giddens T.D., Yadav S.B.: The Development and Evaluation of an Improved Genetic Algorithm Based on Migration and Artificial Selection. IEEE Trans. on Systems, Man, and Cybernetics 24 (1994) 73–86.

    Article  Google Scholar 

  18. Preparata J.F., Vuillemin J.E.: The Cube-Connected Cycles: A Versatile Network for Parallel Computation. Communications of the ACM 24(5), (1981) 300–309.

    Article  MathSciNet  Google Scholar 

  19. Schlierkamp-Voosen D., Mühlenbein H.: Strategy Adaptation by Competing Subpopulations. Parallel Problem Solving from Nature 3, Y. Davidor, H.-P. Schwefel, R. Männer (Eds.) (Berlin, Germany, Springer-Verlag, 1994) 199–208.

    Google Scholar 

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

    Google Scholar 

  21. Tanese R.: Distributed Genetic Algorithms. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo, 1989) 434–439.

    Google Scholar 

  22. Voigt H.M., Born J.: A Structured Distributed Genetic Algorithm for Function Optimization. Parallel Problem Solving from Nature 2, R. Männer, B. Manderick (Eds.) (Elsevier Science Publishers, Amsterdam, 1992) 199–208.

    Google Scholar 

  23. Whitley D., Beveridge R., Graves C., Mathias K.: Test Driving Three 1995 Genetic Algorithms: New Test Functions and Geometric Matching. Journal of Heuristics 1 (1995) 77–104.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Francisco Herrera & Manuel Lozano

  2. Dept. of Computer Science, University of Dortmund, 44221, Dortmund, Germany

    Claudio Moraga

Authors
  1. Francisco Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Lozano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio Moraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Herrera, F., Lozano, M., Moraga, C. (1998). Hybrid distributed real-coded genetic algorithms. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056902

Download citation

  • DOI: https://doi.org/10.1007/BFb0056902

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation