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.
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