Evolving Turing machines for Biosequences Recognition
and Analysis
E. Vallejo, and F. Ramos. Genetic Programming, Proceedings of EuroGP'2001, volume 2038 of LNCS, page 192--203. Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
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 sequences as the working example. Experimental
results indicate that evolved Turing machines are
capable of recognizing HIV sequences in a collection of
training sets. In addition, we demonstrate that the
evolved Turing machines can be used to approximate the
multiple se…(more)
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%0 Conference Paper
%1 vallejo:2001:EuroGP
%A Vallejo, Edgar E.
%A Ramos, Fernando
%B Genetic Programming, Proceedings of EuroGP'2001
%C Lake Como, Italy
%D 2001
%E Miller, Julian F.
%E Tomassini, Marco
%E Lanzi, Pier Luca
%E Ryan, Conor
%E Tettamanzi, Andrea G. B.
%E Langdon, William B.
%I Springer-Verlag
%K Bioinformatics, DNA, Multiple Sequence Turing algorithms, aligment genetic machines, programming,
%P 192--203
%T Evolving Turing machines for Biosequences Recognition
and Analysis
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=192
%V 2038
%X 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 sequences as the working example. Experimental
results indicate that evolved Turing machines are
capable of recognizing HIV sequences in a collection of
training sets. In addition, we demonstrate that the
evolved Turing machines can be used to approximate the
multiple sequence alignment problem.
%@ 3-540-41899-7
@inproceedings{vallejo:2001:EuroGP,
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 sequences as the working example. Experimental
results indicate that evolved Turing machines are
capable of recognizing HIV sequences in a collection of
training sets. In addition, we demonstrate that the
evolved Turing machines can be used to approximate the
multiple sequence alignment problem.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Lake Como, Italy},
author = {Vallejo, Edgar E. and Ramos, Fernando},
biburl = {https://www.bibsonomy.org/bibtex/29bd069abb1e568ef42af83fae7cc931f/brazovayeye},
booktitle = {Genetic Programming, Proceedings of EuroGP'2001},
editor = {Miller, Julian F. and Tomassini, Marco and Lanzi, Pier Luca and Ryan, Conor and Tettamanzi, Andrea G. B. and Langdon, William B.},
interhash = {7ec01fcd5ac14ffd436bac0960d04d18},
intrahash = {9bd069abb1e568ef42af83fae7cc931f},
isbn = {3-540-41899-7},
keywords = {Bioinformatics, DNA, Multiple Sequence Turing algorithms, aligment genetic machines, programming,},
month = {18-20 April},
notes = {EuroGP'2001, part of \cite{miller:2001:gp}},
organisation = {EvoNET},
pages = {192--203},
publisher = {Springer-Verlag},
publisher_address = {Berlin},
series = {LNCS},
size = {12 pages},
timestamp = {2008-06-19T17:53:31.000+0200},
title = {Evolving Turing machines for Biosequences Recognition
and Analysis},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=192},
volume = 2038,
year = 2001
}