Skip to main content

The Virtual Gene Genetic Algorithm

  • Conference paper
  • First Online:
Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

Included in the following conference series:

Abstract

This paper presents the virtual gene genetic algorithm (vgGA) which is a generalization of traditional genetic algorithms that use binary linear chromosomes. In the vgGA, traditional one point crossover and mutation are implemented as arithmetic functions over the integers or reals that the chromosome represents. This implementation allows the generalization to virtual chromosomes of alphabets of any cardinality. Also, the sites where crossover and mutation fall can be generalized in the vgGA to values that do not necessarily correspond to positions between bits or digits of another base, thus implementing generalized digits. Preliminary results that indicate that the vgGA outperforms a GA with binary linear chromosomes on integer and real valued problems where the underlying structure is not binary are presented.

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 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. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  2. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    Google Scholar 

  3. Wright, A.H.: Genetic algorithms for real parameter optimization. In Rawlins, G.J.E., ed.: Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991) 205–218

    Google Scholar 

  4. Eschelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In Whitley, L.D., ed.: Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, CA (1993) 187–201

    Google Scholar 

  5. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 2nd. edn. Springer-Verlag, Berlin (1994)

    MATH  Google Scholar 

  6. Surry, P.D., Radcliffe, N.J.: Real representations. In Belew, R.K., Vose, M.D., eds.: Foundations of Genetic Algorithms 4. Morgan Kaufmann, San Mateo, CA (1997) 187–201

    Google Scholar 

  7. Golbderg, D.E.: Personal communication. (1990)

    Google Scholar 

  8. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In Rawlins, G.J.E., ed.: Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1991) 69–93

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Valenzuela-Rendón, M. (2003). The Virtual Gene Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_18

Download citation

  • DOI: https://doi.org/10.1007/3-540-45110-2_18

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics