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Cluster-based evolutionary design of digital circuits using all improved multi-expression programming

Published:07 July 2007Publication History

ABSTRACT

Evolutionary Electronics (EE) is a research area which involves application of Evolutionary Computation in the domain of electronics. EE algorithms are generally able to find good solutions to rather small problems in a reasonable amount of time, but the need for solving more and more complex problems increases the time required to find adequate solutions. This is due to the large number of individuals to be evaluated and to the large number of generations required until the convergence process leads to the solution. As a consequence, there have been multiple efforts to make EE faster, and one of the most promising choices is to use distributed implementations. In this paper, we propose a cluster-based evolutionary design of digital circuits using a distributed improved multi expression programming method (DIMEP). DIMEP keeps, in parallel, several sub-populations that are processed by Impoved Multi-Expression Programming algorithms, with each one being independent from the others. A migration mechanism produces a chromosome exchange between the subpopulations using MPI (Message Passing Interface) on a dedicated cluster of workstations (Lido Cluster, Dortmund University). This paper presents the main ideas and shows preliminary experimental results.

References

  1. Alba, E. and Tomassini, M., Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation, 6, 5, (2002), 443--462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Banzhaf, W., Nordin, P., Keller, R. E. and Francone, F. D., Generic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bellman, R., Dynamic Programming, Princeton University Press, New Jersey, 1957. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Cantú-Paz, E., A survey of parallel genetic algorithms, Calculateurs Paralleles, Reseaux et Systems Repartis, 10, 2, (1998), 141--171.Google ScholarGoogle Scholar
  5. Cheang, S. M., Leung, K. S., and Lee, K. H., Genetic Parallel Programming: Design and Implementation, Evolutionary Computation, 14, 2, (2006), 129--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Coello, C. A. C., Alba, E. and Luque, G., Comparing Different Serial and Parallel Heuristics to Design Combinational Logic Circuits. In Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, 2003, 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Corcoran, A. L. and Wainwright, R. L., A parallel island model genetic algorithm for the multiprocessor scheduling problem. In Proceedings of the 1994 ACM/SIGAPP Symposium on Applied Computing, 1994, 483--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Fernandez, F. and Tomassini, M., Improving Parallel Genetic Algorithm GA Performance by Means of Plagues, Advances in Soft Computing 2, Springer Verlag, Berlin, Heidelberg, (2005), 515--523.Google ScholarGoogle Scholar
  9. Fernández, F., Tomassini, M. and Vanneschi, L., Studying the influence of Communication Topology and Migration on Distributed Genetic Programming, In J. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. G. B. Tettamanzi, W. Landdon, LNCS 2038 Genetic Programming, 4th European Conference, EuroGP 2001, Springer Verlag, Berlin, (2001), 51--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ferreira, C., Gene Expression Programing: A New Adaptive Algorithm for Solving Problems, Complex Systems, 13, (2001), 87--129.Google ScholarGoogle Scholar
  11. Hadjam F. Z., Moraga C., and Hildebrand L.: Evolutionary design of digital circuits using Improved Multi-Expression Programming. Research Report 812, Faculty of Informatics, University of Dortmund, Germany, 2007. (Copy may be obtained from the authors).Google ScholarGoogle Scholar
  12. Herrera F, Lozano M, Moraga C.: Hybrid distributed real-coded genetic algorithms. Lecture Notes in Computer Science 1498, Springer Verlag, Berlin (1998), 603--612. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Koza, J. R. and Andre, D., Parallel genetic programming on a network of transputers, Proc. of the Workshop on Genetic Programming: From Theory to Real-World Applications. University of Rochester. National Resource Laboratory for the Study of Brain and Behavior. Technical Report 95-2, 111--120, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Koza, J. R., Genetic Programming. On the Programming of Computers by means of Natural Selection, MIT Press, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Miller, J. F., and Thomson, P., Cartesian Genetic Programming. In Proc. of the 3rd International Conference on Genetic Programming (EuroGP2000), LNCS 1082, Springer-Verlag, Berlin, (2000), 15--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Miller, J. F., Job. D. and Vassilev, V. K., Principles in the Evolutionary Design of digitals circuits - Part I, Genetic Programming and Evolvable Machines, 1, 1, (2000), 7--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Oltean M., Solving Even-Parity Problems using Multi Expression Programming, Proceedings of the 5th International Workshop on Frontiers in Evolutionary Algorithms, The 7th Joint Conference on Information Sciences, (September 26-30, 2003, Research Triangle Park, North Carolina), Edited by Ken Chen (et. al), 2003, 315--318.Google ScholarGoogle Scholar
  18. Oltean M., Multi-expression Programming, Technical Report, Babes-Bolyai Univ, Romania, 2006.Google ScholarGoogle Scholar
  19. Oltean, M. and Grosan, C., Solving Classification Problems using Infix Form Genetic Programming, The Fifth International Symposium on Intelligent Data Analysis, edited by M. Berthold (et. al), LNCS 2810, Springer Verlag, Berlin, (2003), 242--252.Google ScholarGoogle ScholarCross RefCross Ref
  20. Oltean, M. and Grosan, C., A Comparison of Several Linear Genetic Programming Techniques, Complex-Systems, 14, 4, (2003), 282--311.Google ScholarGoogle Scholar
  21. Oltean, M. and Grosan, C., Evolving Digital Circuits using Multi Expression Programming. NASA/DoD Conference on Evolvable Hardware, (24-26 June, Seattle), Edited by R. Zebulum (et. al), IEEE Press, NJ, 2004, 87--90.Google ScholarGoogle ScholarCross RefCross Ref
  22. Patterson, N., Genetic Programming with Context-Sensitive Grammars, Ph.D. Thesis, School of Computer Science, University of Scotland, 2002.Google ScholarGoogle Scholar
  23. Ryan, C., O'Neill, M., Grammatical Evolution: A Steady State Approach, Late Breaking Paper, Genetic Programing Edited by J.R. Koza (University of Wisconsin, Madison, Wisconsin, 1998).Google ScholarGoogle Scholar
  24. Skolicki, Z., An Analysis of Island Models in Evolutionary Computation. In Proceeding of the 2005 workshops on Genetic and evolutionary computation, (Washington, D.C. June 2005), 2005, 25--26. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
      July 2007
      1450 pages
      ISBN:9781595936981
      DOI:10.1145/1274000

      Copyright © 2007 ACM

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      • Published: 7 July 2007

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