Skip to main content

Abstract

In this paper the scheduling problem of optimization algorithms is defined. This problem is about scheduling numerical optimization methods from a set of iterative ’oracle-based’ techniques in order to obtain an as efficient as possible optimization process based on the given set of algorithms.

Statements are formulated and proven about the scheduling problem and methods are proposed to solve this problem.

The applicability of one of the proposed methods is demonstrated through a simple fuzzy rule based machine learning example.

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

Access this chapter

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. Snyman, J.A.: Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer, New York (2005)

    MATH  Google Scholar 

  2. Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2(2), 164–168 (1944)

    MATH  MathSciNet  Google Scholar 

  3. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Indust. Appl. Math. 11(2), 431–441 (1963)

    Article  MATH  MathSciNet  Google Scholar 

  4. Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)

    Google Scholar 

  5. Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Transactions on Fuzzy Systems 7(5), 608–616 (1999)

    Article  Google Scholar 

  6. Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.E.: Fuzzy rule extraction by bacterial memetic algorithms. In: Proceedings of the 11th World Congress of International Fuzzy Systems Association, IFSA 2005, Beijing, China, pp. 1563–1568 (2005)

    Google Scholar 

  7. Balázs, K., Botzheim, J., Kóczy, L.T.: Comparison of Various Evolutionary and Memetic Algorithms. In: Proceedings of the International Symposium on Integrated Uncertainty Management and Applications, IUM 2010, Ishikawa, Japan (2010) (accepted for publication)

    Google Scholar 

  8. Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. IEEE Proc. 121(12), 1585–1588 (1974)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Balázs, K., Kóczy, L.T. (2010). A Remark on Adaptive Scheduling of Optimization Algorithms. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14058-7_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14057-0

  • Online ISBN: 978-3-642-14058-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics