Skip to main content
SpringerLink
Log in

Steering of Balance between Exploration and Exploitation Properties of Evolutionary Algorithms - Mix Selection

  • Conference paper
Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

Included in the following conference series:

Abstract

In this paper the novel selection method which can be used in any evolutionary algorithm is presented. Proposed method is based on steering between exploration and exploitation properties of evolutionary algorithms. In presented approach, at the start of the algorithm operation the probability of selection of individuals for new population is equal for all individuals. In such a case the algorithm possesses maximal value of pressure on global search of a solution space (exploration of solution space). As number of generations increases, the algorithm searches the solution space in more locally manner (exploitation of solution space) at expense of global search property. The results obtained using proposed method are compared with the results obtained using other selection methods like: roulette selection, elitist selection, fan selection, tournament selection, deterministic selection, and truncation selection. The comparison is performed using test functions chosen from literature. The results obtained using proposed selection method are better in many cases than results obtained using other selection techniques.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer, Heidelberg (1992)

    MATH  Google Scholar 

  2. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company Inc., New York (1989)

    MATH  Google Scholar 

  3. Arabas, J.: Lectures on evolutionary algorithms, WNT (2001) (in Polish)

    Google Scholar 

  4. Zen, S., Zhou Yang, C.T.: Comparison of steady state and elitist selection genetic algorithms. In: Proceedings of 2004 International Conference on Intelligent Mechatronics and Automation, August 26-31, pp. 495–499 (2004)

    Google Scholar 

  5. Takaaki, N., Takahiko, K., Keiichiro, Y.: Deterministic Genetic Algorithm. In: Papers of Technical Meeting on Industrial Instrumentation and Control, IEE Japan, pp. 33–36 (2003)

    Google Scholar 

  6. Blickle, T., Thiele, L.: A Comparison of Selection Schemes used in Genetic Algorithms. Computer Engineering and Communication Networks Lab, Swiss Federal Institute of Technology, TIK Report, No. 11, Edition 2 (December 1995)

    Google Scholar 

  7. Muhlenbein, H., Schlierkamp-voosen, D.: Predictive Models for the Breeder Genetic Algorithm. Evolutionary Computation 1(1), 2549 (1993)

    Article  Google Scholar 

  8. Slowik, A., Bialko, M.: Modified Version of Roulette Selection for Evolution Algorithm - 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)

    Google Scholar 

  9. Blickle, T., Thiele, L.: A Comparison of Selection Schemes used in Evolutionary Algorithms. Evolutionary Computation 4(4), 361–394 (1996)

    Article  Google Scholar 

  10. Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection and the effects of noise. Complex Systems 9, 193–212 (1995)

    MathSciNet  Google Scholar 

  11. Bäck, T., Hoffmeister, F.: Extended Selection Mechanisms in Genetic Algorithms. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 92–99. Morgan Kaufmann Publishers, San Mateo (1991)

    Google Scholar 

  12. Private communication from Prof. Wojciech Jedruch, Department of Electronics, Telecommunications and Informatics. Gdansk University of Technology (2007)

    Google Scholar 

  13. Zhaoa, X., Gaob, X.-S., Hu, Z.-C.: Evolutionary programming based on non-uniform mutation. Applied Mathematics and Computation 192(1), 1–11 (2007)

    Article  MathSciNet  Google Scholar 

  14. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Technical Report, Nanyang Technological University, Singapore And KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur) (May 2005)

    Google Scholar 

  15. Hansen, N., Kern, S.: Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 282–291. Springer, Heidelberg (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Adam Słowik

Authors
  1. Adam Słowik
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Słowik, A. (2010). 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) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13232-2_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation