Skip to main content
SpringerLink
Log in

Fuzzy Control of Exploration and Exploitation Trade-Off with On-Line Convergence Rate Estimation in Evolutionary Algorithms

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

Included in the following conference series:

  • 1883 Accesses

Abstract

In this paper, the fuzzy control of exploration and exploitation trade-off with on-line convergence rate estimation in evolutionary algorithms is presented. We introduce to the proposed algorithm three fuzzy systems (Mamdani type-1). These fuzzy systems are responsible for controlling the parameters of an evolutionary algorithm, such as selection pressure, crossover probability and mutation probability. While creating the fuzzy rules in the proposed fuzzy systems, we assumed that, at the start, the algorithm should possess maximal exploration property (low selection pressure, high mutation probability, and high crossover probability), while at the end, the algorithm should possesses maximal exploitation property (high selection pressure, low mutation probability, and low crossover probability). Also, in the paper we propose a method for estimating the algorithm convergence rate value. The proposed approach is verified using test functions chosen from literature. The results obtained using the proposed method are compared with the results obtained using evolutionary algorithms with a different selection operator, and standard values for crossover and mutation probability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, S., Mernik, M., Bryant, B.R.: Entropy-driven parameter control for evolutionary algorithms. Informatica 31, 41–57 (2007)

    MATH  Google Scholar 

  2. Hussain, A., Muhammad, Y.S.: Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator. Complex Intell. Syst. 6(1), 1–14 (2019). https://doi.org/10.1007/s40747-019-0102-7

    Article  MathSciNet  Google Scholar 

  3. Xie, H., Zhang, M., Andreae, P.: An analysis of constructive crossover and selection pressure in genetic algorithm. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 1739–1746 (2007)

    Google Scholar 

  4. Back, T.: Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of 1st IEEE Conference on Evolutionary Computing, pp. 57–62 (1994)

    Google Scholar 

  5. Słowik, A.: Steering of balance between exploration and exploitation properties of evolutionary algorithms - mix selection. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 213–220. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13232-2_26

    Chapter  Google Scholar 

  6. Slowik, A.: Fuzzy control of trade-off between exploration and exploitation properties of evolutionary algorithms. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011. LNCS (LNAI), vol. 6678, pp. 59–66. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21219-2_9

    Chapter  Google Scholar 

  7. Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore, Report number 2005005, May 2005

    Google Scholar 

  8. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report 201311, Computational intelligence laboratory, Zhengzhou University, Zhengzhou, China and Technical report, Nanyang Technological University, Singapore, December 2013

    Google Scholar 

  9. Słowik, A., Białko, M.: Modified version of roulette selection for evolution algorithms – the fan selection. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 474–479. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_70

    Chapter  Google Scholar 

  10. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996). https://doi.org/10.1007/978-3-662-03315-9

    Book  MATH  Google Scholar 

  11. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc., New York (1989)

    MATH  Google Scholar 

  12. Smullen, D., Gillett, J., Heron, J., Rahnamayan, S.: Genetic algorithm with self-adaptive mutation controlled by chromosome similarity. In: IEEE Congress on Evolutionary Computation (CEC), Beijing, pp. 504–511 (2014)

    Google Scholar 

  13. Xu, J., Pei, L., Zhu, R.: Application of a genetic algorithm with random crossover and dynamic mutation on the travelling salesman problem. In: 8th International Congress of Information and Communication Technology, ICICT 2018, Procedia Computer Science, vol. 131, pp. 937–945 (2018)

    Google Scholar 

  14. Lin, W., Lee, W., Hong, T.: Adapting crossover and mutation rates in genetic algorithms. J. Inf. Sci. Eng. 19(5), 889–903 (2003)

    Google Scholar 

  15. Varnamkhasti, M.J., Lee, L.S., Bakar, M.R., Leong, W.J.: A genetic algorithm with fuzzy crossover operator and probability. Adv. Oper. Res. 2012 (2012). Article ID 956498, 16 pages

    Google Scholar 

  16. Khmeleva, E., Hopgood, A.A., Tipi, L., Shahidan, M.: Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem. KI - Künstliche Intelligenz 32(1), 61–75 (2017). https://doi.org/10.1007/s13218-017-0516-6

    Article  Google Scholar 

  17. Lapa, K., Cpalka, K.: Flexible fuzzy PID controller (FFPIDC) and a nature-inspired method for its construction. IEEE Trans. Industr. Inf. 14(3), 1078–1088 (2018)

    Article  Google Scholar 

  18. Cpałka, K.: Design of Interpretable Fuzzy Systems. SCI, vol. 684. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52881-6

    Book  Google Scholar 

Download references

Acknowledgment

This work was supported by Polish National Science Center (NCN) under a research grant 2018/02/X/ST6/02475.

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, Koszalin University of Technology, Sniadeckich 2 Street, 75-453, Koszalin, Poland

    Adam Slowik

Authors
  1. Adam Slowik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adam Slowik .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Slowik, A. (2020). Fuzzy Control of Exploration and Exploitation Trade-Off with On-Line Convergence Rate Estimation in Evolutionary Algorithms. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61401-0_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61400-3

  • Online ISBN: 978-3-030-61401-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation