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Parallel Genetic Algorithm Based on Fuzzy Controller for Design Problems

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Artificial Intelligence Perspectives in Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 464))

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Abstract

In this paper a method of joint solutions of placement and routing problems of digital equipment elements is offered. The authors suggested a new approach on the basis of evolutionary algorithm (EA) integration and a fuzzy control model of algorithm parameters. A fuzzy logical controller structure is described in the article. A model of parallel evolutionary algorithm is developed. To synchronize parallel computations, you proposed to use a modified migration operator. To confirm the method effectiveness a brief program description is reviewed.

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References

  1. Shervani, N.: Algorithms for VLSI Physical Design Automation, 538 pp. Kluwer Academy Publisher, USA (1995)

    Google Scholar 

  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)

    Google Scholar 

  3. Gladkov, L.A., Kureichik, V.V., Kureichik, V.M.: Genetic Algorithms. Fizmatlit, Moscow (2010)

    MATH  Google Scholar 

  4. Michael, A., Takagi, H.: Dynamic control of genetic algorithms using fuzzy logic techniques. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 76–83. Morgan Kaufmann (1993)

    Google Scholar 

  5. Lee, M.A., Takagi, H.: Integrating design stages of fuzzy systems using genetic algorithms. In: Proceedings of the 2nd IEEE International Conference on Fuzzy System, pp. 612–617 (1993)

    Google Scholar 

  6. Herrera, F., Lozano, M.: Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. J. Soft Comput. 545–562 (2003)

    Google Scholar 

  7. Liu, H., Xu, Z., Abraham, A.: Hybrid fuzzy-genetic algorithm approach for crew grouping. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA’05), pp. 332–337 (2005)

    Google Scholar 

  8. King, R.T.F.A., Radha, B., Rughooputh, H.C.S.: A fuzzy logic controlled genetic algorithm for optimal electrical distribution network reconfiguration. In: Proceedings of 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, pp. 577–582 (2004)

    Google Scholar 

  9. Im, S.-M., Lee, J.-J.: Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms. Artif. Life Robot. 13(1), 129–133 (2008)

    Article  MathSciNet  Google Scholar 

  10. Rodriguez, M.A., Escalante, D.M., Peregrin, A.: Efficient distributed genetic algorithm for rule extraction. Appl. Soft Comput. 11, 733–743 (2011)

    Article  Google Scholar 

  11. Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE T. Evol. Comput. 6, 443–461 (2002)

    Article  Google Scholar 

  12. Zhongyang, X., Zhang, Y., Zhang, L., Niu, S.: A parallel classification algorithm based on hybrid genetic algorithm. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 3237–3240 (2006)

    Google Scholar 

  13. Gladkov, L., Gladkova, N., Leiba, S.: Manufactoring scheduling problem based on fuzzy genetic algorithm. In: Proceeding of IEEE East-West Design and Test Symposium—(EWDTS’2014). Kiev, Ukraine, pp. 209–212 (2014)

    Google Scholar 

  14. Gladkov, L.A., Gladkova, N.V., Leiba, S.N.: Electronic computing equipment schemes elements placement based on hybrid intelligence approach. Advanced in Intelligent Systems and Computing. In:: Intelligent Systems in Cybernetics and Automation Theory, vol. 348, pp. 35–45. Springer International Publishing, Switzerland (2015)

    Google Scholar 

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Acknowledgments

This research is supported by the Ministry of Education and Science of the Russian Federation, the project # 8.823.2014.

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Authors and Affiliations

  1. Southern Federal University, Rostov-on-Don, Russia

    Leonid Gladkov, Sergey Leyba, Nadezhda Gladkova & Andrey Lezhebokov

Authors
  1. Leonid Gladkov
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  2. Sergey Leyba
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  3. Nadezhda Gladkova
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  4. Andrey Lezhebokov
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Corresponding author

Correspondence to Leonid Gladkov .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Gladkov, L., Leyba, S., Gladkova, N., Lezhebokov, A. (2016). Parallel Genetic Algorithm Based on Fuzzy Controller for Design Problems. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-33625-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33623-7

  • Online ISBN: 978-3-319-33625-1

  • eBook Packages: EngineeringEngineering (R0)

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