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European Conference on Genetic Programming

EuroGP 2001: Genetic Programming pp 192–203Cite as

Evolving Turing Machines for Biosequence Recognition and Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

Abstract

This article presents a genetic programming system for biosequence recognition and analysis. In our model, a population of Turing machines evolves the capability of biosequence recognition using genetic algorithms. We use HIV biosequences as the working example. Experimental results indicate that evolved Turing machines are capable of recognizing HIV biosequences in a collection of training sets. In addition, we demostrate that the evolved Turing machines can be used to approximate the multiple sequence alignment problem.

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Author information

Authors and Affiliations

  1. Computer Science Department, Módulo de Servicio Postal Campus Estado de México del ITESM, ITESM Campus Estado de México, 50, 52926, México, México

    Edgar E. Vallejo

  2. omputer Science Department, ITESM Campus Morelos, Ave. Paseo de la Reforma 182 Col. Lomas de Cuernavaca, 62589, Morelos, México

    Fernando Ramos

Authors
  1. Edgar E. Vallejo
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  2. Fernando Ramos
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Vallejo, E.E., Ramos, F. (2001). Evolving Turing Machines for Biosequence Recognition and Analysis. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_15

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  • DOI: https://doi.org/10.1007/3-540-45355-5_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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