Skip to main content
SpringerLink
Log in

Genetic Folding: A New Class of Evolutionary Algorithms

  • Conference paper
  • First Online:
Research and Development in Intelligent Systems XXVII (SGAI 2010)

Abstract

In this paper, a new class of Evolutionary Algorithm (EA) named as Genetic Folding (GF) is introduced. GF is based on novel chromosomes organisation which is structured in a parent form. In this paper, the model selection problem of Support Vector Machine (SVM) kernel expression has been utilised as a case study. Five UCI datasets have been tested and experimental results are compared with other methods. As a conclusion, the proposed algorithm is very promising and it can be applied to solve further complicated domains and problems.

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. Sivanandam, S. and Deepa, S., 'Introduction to Genetic Algorithm', Springer, 15-130 (2008).

    Google Scholar 

  2. Koza, J. R., 'Genetic Programming: on the Programming of Computers by Means of Natural Selection', 74-147, Cambridge, MA: The MIT Press, (1992).

    MATH  Google Scholar 

  3. Cristianini, N. and Shawe-Taylor, J., 'An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods', 1st ed. Cambridge University Press, (2000).

    Google Scholar 

  4. Dioşan, L., Rogozan, A. and Pecuchet, J-P., 'Optimising Multiple Kernels for SVM by Genetic Programming’, Evolutionary Computation in Combinatorial Optimization, vol. 4972, 230-241 (2008).

    Article  Google Scholar 

  5. Sullivan, K. and Luke, S., 'Evolving Kernels for Support Vector Machine Classification', Genetic And Evolutionary Computation Conference, 1702 – 1707 (2007).

    Google Scholar 

  6. Gagné, C., Schoenauer, M., Sebag, M. and Tomassini, M., 'Genetic Programming for Kernel- based Learning with Co-evolving Subsets Selection', LNCS, no. 4193, 1008-1017 (2006).

    Google Scholar 

  7. Howley, T. and Madden M., 'The Genetic Kernel Support Vector Machine: Description and Evaluation', Artificial Intelligence Review, vol. 24, no. 3-4, 379-395 (2005).

    Google Scholar 

  8. Fawcett, T., 'An Introduction to ROC Analysis', Pattern Recognition Letters, vol. 27, no. 8, 861-874 (2006).

    Article  MathSciNet  Google Scholar 

  9. Diosan, L., Rogozan, A. and Pecuchet, J-P. , 'Evolving Kernel Functions for SVMs by Genetic Programming', Machine Learning and Applications, ICMLA, 19-24 (2007).

    Google Scholar 

  10. Chen, P-W., Wang, J-Y. and Lee, H-M., 'Model Selection of SVMs Using GA Approach', IEEE International Joint Conference, vol. 3, 2035-2040 (2004).

    Google Scholar 

  11. Staelin C., 'Parameter Selection for Support Vector Machines', HP Laboratories, (2003).

    Google Scholar 

  12. Lessmann, S., Stahlbock, R. and Crone, S. F., 'Genetic Algorithms for Support Vector Machine Model Selection', Proc. of the Intern. Joint Conf. on Neural Networks (IJCNN'06), Vancouver, Canada, (2006).

    Google Scholar 

  13. Rojas, S.A. and Fernandez-Reyes, D., 'Adapting Multiple Kernel Parameters for Support Vector Machines using Genetic Algorithms', IEEE, vol. 1. 626-631 (2005).

    Google Scholar 

  14. Silva S., 'GPLAB: A Genetic Programming Toolbox for MATLAB', (2007).

    Google Scholar 

  15. "Genes & Gene Expression". The Virtual Library of Biochemistry and Cell Biology. Bio-ChemWeb.org. 2010-02-08.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Brunel University, West London, Uxbridge, UB8 3PH, UK

    M.A. Mezher & M.F. Abbod

Authors
  1. M.A. Mezher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M.F. Abbod
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M.A. Mezher or M.F. Abbod .

Editor information

Editors and Affiliations

  1. Dept. Computer Science and, Software Engineering, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , Faculty of Technology, De Montford University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Adrian Hopgood

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this paper

Cite this paper

Mezher, M., Abbod, M. (2011). Genetic Folding: A New Class of Evolutionary Algorithms. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-130-1_21

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-129-5

  • Online ISBN: 978-0-85729-130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation