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Electronic Computing Equipment Schemes Elements Placement Based on Hybrid Intelligence Approach

  • L. A. GladkovEmail author
  • N. V. Gladkova
  • S. N. Leiba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 348)

Abstract

The problem of electronic computing equipment (ECE) schemes elements placement within a switching field is considered in this article. It refers to the class of design problems that are NP-hard and NP-full. The authors suggested a new approach on the basis of genetic algorithm (GA) integration and a fuzzy control model of algorithm parameters. A fuzzy logical controller structure is described in the article. To confirm the method effectiveness a brief program description is reviewed.

Keywords

ECE Design Elements placement Optimization Genetic algorithm Fuzzy logic 

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References

  1. 1.
    Shervani, N.: Algorithms for VLSI physical design automation, 538 p. Kluwer Academy Publisher, USA (1995)Google Scholar
  2. 2.
    Cohoon, J.P., Karro, J., Lienig, J.: Evolutionary Algorithms for the Physical Design of VLSI Circuits. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 683–712. Springer, London (2003)CrossRefGoogle Scholar
  3. 3.
    Herrera, F., Lozano, M.: Fuzzy Adaptive Genetic Algorithms: design, taxonomy, and future directions. J. Soft Computing, 545–562 (2003)Google Scholar
  4. 4.
    Gladkov, L.A.: An integrated algorithm for solving the placement and the track-lock of the basis of fuzzy genetic methods. J. Izvestiya SFedU. Engineering Sciences 120, 22–30 (2011)Google Scholar
  5. 5.
    Gladkov, L.A., Kureichik, V.V., Kureichik, V.M.: Genetic algorithms. Fizmatlit, Moscow (2010)Google Scholar
  6. 6.
    Kureichik, V.M.: Modified genetic operators. J. Izvestiya SFedU. Engineering Sciences 12, 7–15 (2009)Google Scholar
  7. 7.
    Gladkov, L., Gladkova, N., Leiba, S.: Manufactoring scheduling problem based on fuzzy genetic algorithm. In: Proceeding of IEEE East-West Design & Test Symposium – (EWDTS 2014), Kiev, Ukraine, pp. 209–212 (2014)Google Scholar
  8. 8.
    Yarushkina, N.G.: Foundations of the theory of fuzzy and hybrid systems. Finance and Statistics, Moscow (2004)Google Scholar
  9. 9.
    Batyrshin, I.Z., Nedosekin, S.A.: Fuzzy hybrid systems. Theory and practice. Fizmatlit, Moscow (2007)zbMATHGoogle Scholar
  10. 10.
    Gladkov, L.A., Gladkova, N.V.: New approaches to the construction of systems analysis and knowledge extraction based on hybrid methods. J. Izvestiya SFedU. Engineering Sciences. 108, 146–154 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Southern Federal UniversityRostov-on-DonRussia

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