Skip to main content
SpringerLink
Log in

Simulated Evolution of the Adaptability of the Genetic Code Using Genetic Algorithms

  • Conference paper
Bio-inspired Modeling of Cognitive Tasks (IWINAC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4527))

Abstract

In this work we use simulated evolution to corroborate the adaptability of the natural genetic code. An adapted genetic algorithm searches for optimal hypothetical codes. The adaptability is measured as the average variation of the hydrophobicity that experiment the encoded amino acids when errors or mutations are presented in the codons of the hypothetical codes. Different types of mutations and base position mutation probabilities are considered in this study.

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 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. Gilis, D., Massar, S., Cerf, N.J., Rooman, M.: Optimality of the genetic code with respect to protein stability and amino-acid frequencies. Genome Biology 2(11) (2001)

    Google Scholar 

  2. Haig, D., Hurst, L.D.: A quantitative measure of error minimization in the genetic code. Journal of Molecular Evolution 33, 412–417 (1991)

    Article  Google Scholar 

  3. Yockey, H.P.: Information Theory, Evolution, and the Origin of Life. Cambridge University Press, Cambridge (2005)

    MATH  Google Scholar 

  4. Giulio, M.D.: The origin of the genetic code: theories and their relationship, a review. Biosystems 80, 175–184 (2005)

    Article  Google Scholar 

  5. Knight, R.D., Freeland, S.J., Landweber, L.F.: Adaptive evolution of the genetic code. The Genetic Code and the Origin of Life 80, 175–184 (2004)

    Google Scholar 

  6. Freeland, S.J.: The darwinian genetic code: an adaptation for adapting? Genetic Programming and Evolvable Machines 3, 113–127 (2002)

    Article  MATH  Google Scholar 

  7. Freeland, S.J., Hurst, L.D.: The genetic code is one in a million. Journal of Molecular Evolution 47(3), 238–248 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Computación, Facultade de Informática. Universidade da Coruña., Campus de Elviña, 15071 A Coruña, Spain

    Ángel Monteagudo & José Santos

Authors
  1. Ángel Monteagudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

José Mira José R. Álvarez

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Monteagudo, Á., Santos, J. (2007). Simulated Evolution of the Adaptability of the Genetic Code Using Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73053-8_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73052-1

  • Online ISBN: 978-3-540-73053-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation