Advertisement

Parallel simulated annealing and genetic algorithms: A space of hybrid methods

  • Hao Chen
  • Nicholas S. Flann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

Simulated annealing and genetic algorithms represent powerful optimization methods with complementary strengths and weaknesses. Hence, there is an interest in identifying hybrid methods (which combine features of both SA and GA) that exhibit performance superior than either method alone. This paper introduces a systematic approach to identifying these hybrids by defining a space of methods as a nondeterministic generating grammar. This space includes SA, GA, previously introduced hybrids and many new methods. An empirical evaluation has been completed for 14 methods from this space applied to 9 diverse optimization problems. Results demonstrate that the space contains promising new methods. In particular, a new method that combines the recombinative power of GAs and annealing schedule of SA is shown to be one of the best methods for all 9 optimization problems explored.

Keywords

Genetic Algorithm Simulated Annealing Hybrid Method Neighborhood Operator Cooling Schedule 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AaKo89]
    Aarts E., & Korst, J. (1989). Simulated Annealing and Boltzmann Machines: A stochastic approach to Combinatorial Optimization and Neural Computing. Wiley, Chichester.Google Scholar
  2. [Bäck91]
    Thomas Bäck (1991). A User's Guide to GENEsYs University of Dortmund Department of Computer Science Systems Analysis Research Group P.O. Box 50 05 00 D-4600 Dortmund 50 July 1st, 1992 Google Scholar
  3. [BEE87]
    Boseniuk T., Ebeling W. and Engel A. (1987). Boltsmann and darwin strategies in complex optimization, in Physics Letters A, Vol. 125 (6.7).Google Scholar
  4. [BE88]
    Boseniuk T., and Ebeling W. (1988). Optimization of NP-complete problems by Boltzmann-Darwin strategies including life-cycles. Europhysics Letters, 6(2) 107–112.Google Scholar
  5. [BE91]
    Boseniuk T., and Ebeling W. (1991). Boltzmann-, Darwin-, and Haeckel-strategies in optimization problems. Lecture Notes in Computer Science: Parallel Problem Solving from Nature, 496, pp. 430–444.Google Scholar
  6. [BHS89]
    Brown, D. E., Huntley C. L and Spillane A. R. (1989). A parallel genetic heuristic for the quadratic assignment problem. Proceedings of the Third International Conference on Genetic Algorithms, pp. 406–415.Google Scholar
  7. [Coh-91]
    James P. Cohoon, Shailesh U. Hegde, Worthy N. Martin, and Dana S. Richards. Distributed Genetic Algorithms for the Floorplan Design Problem IEEE Trans. on Computer Aided Design, Vol 10, No. 4, April 1991, pp. 483–491CrossRefGoogle Scholar
  8. [EAV91]
    Eiben A. E., Aarts E. H. L. and Van Hee K. M. (1991). Global Convergence of genetic algorithms: a Markov chain analysis. Lecture Notes in Computer Science: Parallel Problem Solving from Nature, 496, 4–12.Google Scholar
  9. [FoHu91]
    Fogarty T. C. and Huang R. (1991) Implementing the genetic algorithm on transcomputer based parallel processing systems. In Lecture Notes in Computer Science: Parallel Problem Solving from Nature, pp. 145–149.Google Scholar
  10. [Gol89]
    Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley Publishing Company, Inc. 1989Google Scholar
  11. [Gol90]
    Goldberg D. E. (1990). A note on Boltzman tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems, 4, 445–460.Google Scholar
  12. [Gre90]
    Green D. R. (1990). Parallel Simulated Annealing Techniques. Physica D 42 293–306.Google Scholar
  13. [HB91]
    Hoffmeister F. and Bäck T. (1991). Genetic self-learning. In Proceedings of the First European Conference on Artificial Life, December 11–13, Paris, France, 1991. MIT Press.Google Scholar
  14. [HCH91]
    Hoffmann, K. H., Christoph, M. & Hanf, M. (1991). Optimizing simulated annealing. Lecture Notes in Computer Science: Parallel Problem Solving from Nature, 496, pp. 221–225.Google Scholar
  15. [Hol75]
    Holland, J. H. Adaptation in natural and artificial systems, Cambridge: MIT Press.Google Scholar
  16. [InRo92]
    Ingber L. and Rosen B. (1992). Genetic algorithms and very fast simulated reannealing: A comparison. in Mathematical Computer Modeling, Vol. 16. Noo. 11. pp. 87–100.CrossRefGoogle Scholar
  17. [Jon75]
    De Jong K. (1975). An analysis of the behavior 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
  18. [KFM71]
    Krolark P., Felts W. & Marble G. (1971). A man-machine approach to solving the traveling salesman problem. Communications of the ACM, 14(4): 327–224.CrossRefGoogle Scholar
  19. [LKH91]
    Lin F. T., Kao C. Y. &: Hsu C. C. (1991). Incorporating genetic algorithms into simulated annealing. Proceedings of the Fourth International Symposium on Artificial Intelligence, 290–297.Google Scholar
  20. [MG92]
    Mahfoud S. W. and Goldberg D. E. (1992). Parallel recombinative simulated annealing: A genetic algorithm, in IlliGAL Report No. 92002.Google Scholar
  21. [Ru94]
    Rudolph G. (1994). Convergence properties of canonical genetic algorithms, in IEEE Transactions on Neural Networks 5 (1) pp. 96–101, 1994.CrossRefGoogle Scholar
  22. [Sch81]
    Schwefel H. P. (1981). Numerical Optimization of Computer Models, Wiley, Chichester, 1981.Google Scholar
  23. [SiWe87]
    Sirag D. J. and Weisser P. T. (1987). Towards a unified thermodynamic genetic operator. Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms, 116–122.Google Scholar
  24. [Sti93]
    Stiles G. S. (1993). On the Speedup of Simultaneously executed randomized algorithms, in IEEE Transactions on Parallel and Distributed Systems, 1993.Google Scholar
  25. [TöŽi89]
    Törn A. and Žilinskas A., 1989. Global Optimization, Volume 350 of Lecture Notes in Computer Science. Springer, Berlin, 1989.Google Scholar
  26. [VAL92]
    Vaessens, Aarts & Lenstra (1992). A local search template, in Parallel Problem Solving from Nature 2, North Holland, pp. 65–74.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Hao Chen
    • 1
  • Nicholas S. Flann
    • 1
  1. 1.Department of Computer ScienceUtah State UniversityLoganUSA

Personalised recommendations