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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Aarts E., & Korst, J. (1989). Simulated Annealing and Boltzmann Machines: A stochastic approach to Combinatorial Optimization and Neural Computing. Wiley, Chichester.
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
Boseniuk T., Ebeling W. and Engel A. (1987). Boltsmann and darwin strategies in complex optimization, in Physics Letters A, Vol. 125 (6.7).
Boseniuk T., and Ebeling W. (1988). Optimization of NP-complete problems by Boltzmann-Darwin strategies including life-cycles. Europhysics Letters, 6(2) 107–112.
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.
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.
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. 483–491
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.
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.
Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley Publishing Company, Inc. 1989
Goldberg D. E. (1990). A note on Boltzman tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems, 4, 445–460.
Green D. R. (1990). Parallel Simulated Annealing Techniques. Physica D 42 293–306.
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.
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.
Holland, J. H. Adaptation in natural and artificial systems, Cambridge: MIT Press.
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.
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.
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.
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.
Mahfoud S. W. and Goldberg D. E. (1992). Parallel recombinative simulated annealing: A genetic algorithm, in IlliGAL Report No. 92002.
Rudolph G. (1994). Convergence properties of canonical genetic algorithms, in IEEE Transactions on Neural Networks 5 (1) pp. 96–101, 1994.
Schwefel H. P. (1981). Numerical Optimization of Computer Models, Wiley, Chichester, 1981.
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.
Stiles G. S. (1993). On the Speedup of Simultaneously executed randomized algorithms, in IEEE Transactions on Parallel and Distributed Systems, 1993.
Törn A. and Žilinskas A., 1989. Global Optimization, Volume 350 of Lecture Notes in Computer Science. Springer, Berlin, 1989.
Vaessens, Aarts & Lenstra (1992). A local search template, in Parallel Problem Solving from Nature 2, North Holland, pp. 65–74.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, H., Flann, N.S. (1994). Parallel simulated annealing and genetic algorithms: A space of hybrid methods. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_286
Download citation
DOI: https://doi.org/10.1007/3-540-58484-6_286
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58484-1
Online ISBN: 978-3-540-49001-2
eBook Packages: Springer Book Archive