Skip to main content
SpringerLink
Log in

Predicting the Solutions of a Challenging NLP Problem with Asynchronous Parallel Evolutionary Modeling Algorithm

  • Conference paper
  • First Online:
Parallel and Distributed Processing and Applications (ISPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2745))

  • 396 Accesses

Abstract

In this paper, the asynchronous parallel evolutionary modeling algorithm (APEMA) is used to predict the solutions of a challenging non-linear problem (NLP)-a very high dimensional BUMP problem. Numerical experiments shows that the low order ordinary differential equations (ODE) models give good results.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Fayyad U.M, Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.), Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press, 1966.

    Google Scholar 

  2. Cao, H,Q., Kang, LS., Chen, Y.P., and Yu, Z.X., “Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming”, Genetic Programming and Evolvable Machines, Vol.1, No.4, 2000, pp. 309–337.

    Article  MATH  Google Scholar 

  3. Kang Z., Liu P., Kang L.S., Parallel Evolutionary Modeling for Nonlinear Ordinary Differential Equation, Wuhan University Journal of Natural Sciences, Vol.6, No.3 (2001), 659–664.

    MATH  Google Scholar 

  4. Keane, A.J., Experiences with Optimizers in Structural Design, in Proc. of the Conf. on Adaptive Computing in Engineering Design and Control 94, ed. Parmee, I.C., Plymouth, 1994, pp. 14–27.

    Google Scholar 

  5. Koza, J. R., Genetic Programming: on the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.

    MATH  Google Scholar 

  6. Koza, J. R., Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, MA: MIT Press, 1994.

    MATH  Google Scholar 

  7. Koza, J.R., Bennett, F.H, III; Andre, D. and Keane, M. A., Genetic Programming III: Darwinian Invention and Problem Solving, San Francisco, Morgan Kaufmann, 1999.

    MATH  Google Scholar 

  8. Liu, P., Evolutionary Algorithms and Their Parallelization, Doctoral Dissertation, Wuhan University, 2000.

    Google Scholar 

  9. Ferreira, C., Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87–129. 2001.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, China

    Yan Li

  2. Faculty of Human Environment, Nagasaki Institute of Applied Science, 536 Aba-machi, Nagasaki, 851-0193, Japan

    Zhen Liu

Authors
  1. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Software, The University of Aizu, Aizu-Wakamatsu City, Fukushima, 965-8580, Japan

    Minyi Guo

  2. Department of Computer Science, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada

    Laurence Tianruo Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, Y., Liu, Z. (2003). Predicting the Solutions of a Challenging NLP Problem with Asynchronous Parallel Evolutionary Modeling Algorithm. In: Guo, M., Yang, L.T. (eds) Parallel and Distributed Processing and Applications. ISPA 2003. Lecture Notes in Computer Science, vol 2745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-37619-4_30

Download citation

  • DOI: https://doi.org/10.1007/3-540-37619-4_30

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40523-8

  • Online ISBN: 978-3-540-37619-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation