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Comparison of Permutation-Based and Binary Representation in a Genetic Algorithm for RNA Secondary Structure Prediction

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Advances in Artificial Intelligence (Canadian AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3060))

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Abstract

RNA is an important molecule as it serves a key role in the translation from the genetic information encoded in DNA in protein synthesis. Computational techniques for RNA folding suffer from combinatorial explosion. In this paper, a genetic algorithm (GA) will be used to attempt to solve the secondary structure prediction of RNA molecules.

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References

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Author information

Authors and Affiliations

  1. Information Technology, Simon Fraser University, 2400 Central City, 10153 King George Highway, Surrey, B.C., V3T 2W1, CANADA

    Alain Deschênes, Kay C. Wiese & Edward Glen

Authors
  1. Alain Deschênes
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  2. Kay C. Wiese
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  3. Edward Glen
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Editor information

Editors and Affiliations

  1. University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada

    Ahmed Y. Tawfik

  2. School of Computer Science, University of Windsor, Windsor, Ontario,  

    Scott D. Goodwin

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© 2004 Springer-Verlag Berlin Heidelberg

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Deschênes, A., Wiese, K.C., Glen, E. (2004). Comparison of Permutation-Based and Binary Representation in a Genetic Algorithm for RNA Secondary Structure Prediction. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_53

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  • DOI: https://doi.org/10.1007/978-3-540-24840-8_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22004-6

  • Online ISBN: 978-3-540-24840-8

  • eBook Packages: Springer Book Archive

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