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A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization

  • Pu Liu
  • Francis Lau
  • Michael J. Lewis
  • Cho-li Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16][17], and finds a better solution to G10 than [17].

Keywords

Genetic Algorithm Benchmark Problem Markov Chain Model Island Model Parallel Genetic Algorithm 
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.

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References

  1. 1.
    H. T. Yang, “A Parallel Genetic Algorithm Approach to Solving the Unit Commitment Problem: Implementation on the Transputer Networks,” IEEE Transactions on Power Systems, Vol. 12, No.2, pp. 661–668, 1997.CrossRefGoogle Scholar
  2. 2.
    A. Wu, K. Y. Wu, R. M. M. Chen, Y. Shen, “Parallel Optimal Statistical Design Method with Response surface modeling using genetic algorithms,” Circuits, Devices and Systems, IEE Proceedings-, Vol. 145, No.1, 1998.Google Scholar
  3. 3.
    J. D. Lohn, “A Circuit Representation Technique for Automated Circuit Design,” IEEE Transactions on Evolutionary Computation, Vol. 3, No. 3, pp. 205–219, 1999.CrossRefGoogle Scholar
  4. 4.
    E. Cantu-Paz, “Markov Chain Models of Parallel Genetic Algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 4, No. 3, pp. 216–226, 2000.CrossRefGoogle Scholar
  5. 5.
    P. B. Grosso, “Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multi-locus Model,” Ph.D. Dissertation, University of Michigan, 1985.Google Scholar
  6. 6.
    L. A. Anbarasu et al., “Multiple Sequence Alignment by Parallely Evolvable Genetic Algorithms,” in A. S. Wh(ed.) Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program, Orlando, Fla, July 13, 1999, pp. 154–156.Google Scholar
  7. 7.
    H. Lienig, “A Parallel Genetic Algorithm for Performance-Driven VLSI Routing,” IEEE Transactions on Evolutionary Computation, Vol. 1, No.1, pp. 29–39, 1997.CrossRefGoogle Scholar
  8. 8.
    K. Kojima, W. Kawamata, et al, “Network Based Parallel Genetic Algorithm using Client-Server Model”, in Proceedings of the Conference on Evolutionary Computation 2000, pp. 244–250.Google Scholar
  9. 9.
    T. Guo, L. S. Kang, “A New Evolutionary Algorithm for Function Optimization,” Wuhan University Journal of Natural Sciences, Vol. 4, No. 4, pp. 409–414, 1999.zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Z. Michalewicz and M. Schoenauer, “Evolutionary Algorithms for Constrained Parameter Optimization Problems,” Evolutionary Computation, Vol. 4, No. 1, pp. 1–32, 1996.CrossRefGoogle Scholar
  11. 11.
    A. J. Keane, “Experiences with Optimizers in Structural Design,” in Proceedings of the Conference on Adaptive Computing in Engineering Design and Control 94, ed. (I. C. Parmee, Plymouth, 1994), pp. 14–27.Google Scholar
  12. 12.
    A. J. Keane, “A Brief Comparison of Some Evolutionary Optimization Methods,” Proceedings of Applied Decision Technologies (Modern Heuristic Methods), 1995.Google Scholar
  13. 13.
    A. J. Keane, “Genetic Algorithm Optimization of Multi-peak Problems: Studies in Convergence and Robustness.” Artificial Intelligence in Engineering, 1995.Google Scholar
  14. 14.
    A. J. Keane, “Passive Vibration Control Via Unusual Geometric: Application of Genetic Algorithm Optimization to Structural Design,” Journal of Sound and Vibration, 1995.Google Scholar
  15. 15.
    M. A. EI-Beltagy, et al. “Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations.” Genetic Algorithms and Classifier Systems, 1999.Google Scholar
  16. 16.
    Z. Michalewicz, S. Esguvel et al., “The Spirit of Evolutionary Algorithms.” Journal of Computing and Information Technology, Vol. 7, pp. 1–18, 1999.Google Scholar
  17. 17.
    M. J. Tahk, B. C. Sun, “Coevolutionary Augmented Lagrangian Methods for Constrained Optimization.” IEEE Transactions on Evolutionary Computation, Vol. 4, No. 2, 2000.Google Scholar
  18. 18.
    Sunderam, V. S., “PVM: A Framework for Parallel Distributed Computing.” Concurrency: Practice and Experience, Vol. 2, No. 4, pp. 315–339, December, 1990.CrossRefGoogle Scholar
  19. 20.
    Heinz Muhlenbein, “Evolution in Time and Space—The Parallel Genetic Algorithm”, In Gregory J. E. Rawlins, Editor, Foudation of Genetic Algorithms 1, Page 316–337, San Mateo, CA, USA, 1991, Morgan, Kaufmann.Google Scholar
  20. 21.
    William E. Hart, Scott Baden, Richard K. Belew, Scott Kohn, “Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm”, Proc 10th Intl. Parallel Processing Symp. pp 606–612, 1996.Google Scholar
  21. 22.
    Enrique Alba, Jos M Troya Dpto. de Lenguajes y Ciencias de la Computation, “An Analysis of Synchronous and Asynchronous Parallel Distributed Genetic Algorithms with Structured and Panmictic Islands”, Future Generation Computer Systems, 17(4):451–465, January 2001.zbMATHCrossRefGoogle Scholar
  22. 23.
    Grefenstette J. J., “Parallel adaptive algorithms for function optimization”, Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN, 1981.Google Scholar
  23. 24.
    Martin F. J., Trelles-Salazar O., Snadoval F., “Genetic algorithms on LAN-message passing architectures using PVM: Application to the routing problem”, In Davidor Y. Schwefel H.-P., Manner R., Eds., Parallel Problem Solving from Nature, PPSN III, p.534–543, Springer-Verlag (Berlin), 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Pu Liu
    • 1
  • Francis Lau
    • 2
  • Michael J. Lewis
    • 1
  • Cho-li Wang
    • 2
  1. 1.Department of Computer ScienceBinghamton University—SUNYBinghamtonUSA
  2. 2.Department of Computer Science and Information SystemsThe University of Hong KongHong Kong

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