Skip to main content
SpringerLink
Log in

Identification of a Fuzzy Measure by an Evolutionary Strategy

  • Conference paper
Soft Computing as Transdisciplinary Science and Technology

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

  • 891 Accesses

Abstract

Evolutionary strategies are heuristic algorithms mimicking natural evolution processes. They have been developed to solve non-linear optimization problems. We employ an evolutionary strategy to solve identification problem of a fuzzy measure on a finite set. We formulate the problem as an non-linear minimizing problem and report some results of numerical experiments.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Sugeno M (1974)CTheory of fuzzy integrals and its applications. Doctoral Thesis, Tokyo Institute of Technology, Tokyo

    Google Scholar 

  2. Wang Z and Klir G J (1992), Fuzzy Measure Theory, Plenum Press, New York NY

    MATH  Google Scholar 

  3. Wierzchń S T (1983), An Algorithm fof Identification of Fuzzy Measure, Fuzzy Sets and Systems, 9:69–78

    Article  MathSciNet  Google Scholar 

  4. Lee K-M, Lee-Kwang H (1995), Identification of lambda-fuzzy measures by genetic algorithms, Fuzzy Sets and Systems, 75:301–309

    Article  MATH  MathSciNet  Google Scholar 

  5. Bäck T and Schwefel H-P (1993), An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation I(1):1–23

    Google Scholar 

  6. Fogel D B (1995), Evolutionary Computation, IEEE Press, Piscataway, NJ

    Google Scholar 

  7. Rechenberg I (1965), Cybernetic Solution Path of an Experimental Problem, Royal Aircraft Establishment, Library Translation No.1122, August

    Google Scholar 

  8. Schwefel H-P (1965), Kybernetische Evolution als Strategie der Experimentellen Forschung in der Strömungstechnik, Diploma Thesis, Technical University of Berlin

    Google Scholar 

  9. Rechenberg I (1973), Evolutionsstrategie: Optimierung Technisher Systeme nach Prinzipien der Biologischen Evokution, Fromman-Holzboog Verlag, Stuttgart

    Google Scholar 

  10. Schwefel H-P (1987), Collective Phenomena in Evolutionary Systems. In Preprints of the 31st Annual Meeting of International Society for General System Research, Budapest, 2:1025–1033

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hokkaido University of Education at Asahikawa, 9 Hokumon-cho Asahikawa, 070-8621, Japan

    Taka’aki Wakabayashi

  2. Hokkaido Institute of Technology, 4-1, 7-15 Maeda, Teine-ku, Sapporo, 006-8585, Japan

    Tamotsu Mitamura

Authors
  1. Taka’aki Wakabayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tamotsu Mitamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Oklahoma State University, North Greenwood Avenue 700, 74106, Tulsa, USA

    Ajith Abraham

  2. Dept. of Computer Science and Systems Engineering, Muroran Institute of Technology, Mizumoto-cho 27-1, 050-8585, Muroran, Hokkaido, Japan

    Yasuhiko Dote

  3. Dept. of Computational Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi-ken, Japan

    Takeshi Furuhashi

  4. Institut fĂĽr Sicherheits-/PrĂĽftechnik, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  5. Division of Complex Systems Technology Graduate School of Engineering, University of Hokkaido, N-13, W-8 060 KITA-KU, Sapporo, Japan

    Azuma Ohuchi

  6. PRESTO Japan Science & Technology Corp. GSSM, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 12-0012, Tokyo, Japan

    Yukio Ohsawa

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wakabayashi, T., Mitamura, T. (2005). Identification of a Fuzzy Measure by an Evolutionary Strategy. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_20

Download citation

  • DOI: https://doi.org/10.1007/3-540-32391-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation