Adaptive And Cooperative Harmony Search Models For RNA Secondary Structure Prediction
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Date
2011-02
Authors
Mohammed A. Mohsen, Abdulqader
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Abstract
Determining the function of RNA molecules relies heavily on its secondary structure. The
current physical methods for secondary structure determination are expensive and time consuming.
Several algorithms have been proposed for the RNA secondary structure prediction,
including dynamic programming and metaheuristic algorithms. Harmony search (HS) is a new
metaheuristic algorithm which succeeded in solving many different types of optimization problems.
This research proposes three new variants of HS algorithm to address the RNA secondary
structure prediction problem. The first variant is called HSRNAFold as a first application of
HS for RNA secondary structure prediction. The second variant, AHSRNAFold, improves
HSRNAFold by using adaptive parameter control. The third variant, CHSRNAFold, improves
HSRNAFold by using a cooperative multiple harmony memories model. The behavior of the
new HS variants is investigated and the impact of tuning the different parameters of these variants
is evaluated. The experiments were conducted on 20 individuals with known structures
from four RNA classes. The prediction accuracy was verified with native structures and other
state-of-the-art algorithms. The results demonstrate that CHSRNAFold outperformed several
state-of-the-art algorithms in terms of prediction accuracy.
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Harmony Search Models