Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2018: Artificial Intelligence and Soft Computing pp 495–504Cite as

Hybrid Evolutionary System to Solve Optimization Problems

  • Conference paper
  • First Online:

  • 2136 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

Abstract

The article presents an Evolutionary System designed to solve optimization problems. The system consists of Genetic Algorithm and Evolutionary Strategy, working together to improve the efficiency of optimization and increase the resistance to stuck to suboptimal solutions. In the system, we combined the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. The system maintains the right balance between the ability to explore and exploit the search space. Genetic Algorithm and Evolutionary Strategy can exchange information about the solutions found till now and periodically migrate the best individuals between populations. The efficiency of the system has been investigated by an example of function optimization. The results of the experiments suggest that the proposed system can be an effective tool in solving complex optimization problems.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Bäck, T., Hoffmeister, F., Schwefel, H.-P.: A survey of evolution strategies. In: Proceedings of the Fourth International Conference on Genetic Algorithms, vol. 2, no. 9. Morgan Kaufmann (1991)

    Google Scholar 

  2. Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. J. Nat. Comput. 1(1), 3–52 (2002)

    Article  MathSciNet  Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning Reading. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  4. Jensi, R., Jiji, G.W.: An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Appl. Soft Comput. 46, 230–245 (2016)

    Article  Google Scholar 

  5. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1992). https://doi.org/10.1007/978-3-662-07418-3

    Book  MATH  Google Scholar 

  6. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  7. Kwasnicka, H.: Evolutionary Computation in Artificial Intelligence. Publishing House of the Wroclaw University of Technology, Wroclaw (1999). (in Polish)

    Google Scholar 

  8. Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_269

    Chapter  Google Scholar 

  9. Pytel, K., Nawarycz, T.: Analysis of the distribution of individuals in modified genetic algorithms. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 197–204. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13232-2_24

    Chapter  Google Scholar 

  10. Pytel, K.: The fuzzy genetic strategy for multiobjective optimization. In: Proceedings of the Federated Conference on Computer Science and Information Systems, Szczecin (2011)

    Google Scholar 

  11. Pytel, K., Nawarycz, T.: The fuzzy-genetic system for multiobjective optimization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC/SIDE -2012. LNCS, vol. 7269, pp. 325–332. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29353-5_38

    Chapter  Google Scholar 

  12. Pytel, K., Nawarycz, T.: A fuzzy-genetic system for ConFLP problem. In: Advances in Decision Sciences and Future Studies, vol. 2. Progress & Business Publishers, Krakow (2013)

    Google Scholar 

  13. Pytel, K.: Hybrid fuzzy-genetic algorithm applied to clustering problem. In: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, Gdańsk (2016) https://doi.org/10.15439/2016F232

  14. Pytel, K.: Hybrid multievolutionary system to solve function optimization problems. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, Prague, Czech Republik (201). https://doi.org/10.15439/2017F85

  15. Rutkowska, D.: Intelligent Computational Systems. Academic Publishing House PLJ, Warsaw (1997)

    Google Scholar 

  16. Rutkowska, D., Pilinski, M., Rutkowski, L.: Neural Networks, Genetic Algorithms and Fuzzy Systems. PWN Scientific Publisher, Warsaw (1997)

    Google Scholar 

  17. Test Functions Index. http://infinity77.net/global_optimization/test_functions.html

  18. Virtual Library of Simulation Experiments: Test Functions and Datasets. http://www.sfu.ca/~ssurjano/optimization.html

Download references

Author information

Authors and Affiliations

  1. Faculty of Physics and Applied Informatics, University of Lodz, Łódź, Poland

    Krzysztof Pytel

Authors
  1. Krzysztof Pytel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Pytel .

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. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pytel, K. (2018). Hybrid Evolutionary System to Solve Optimization Problems. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91253-0_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation