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The use of genetic algorithms for non-coding RNA gene finding has previously been investigated and found to be a potentially viable method for accelerating covariance-model-based database search relative to full dynamic-programming methods. The mutation operators in previous work chose new alignment insertion and deletion locations uniformly over the length of the model consensus sequence. Since the covariance models are estimated from multiple known members of a non-coding RNA family, information is available as to the likelihood of insertions or deletions at the individual model positions. This information is implicit in the state-transition parameters of the estimated covariance models. In the current work, the use of mutation operators which are biased toward selection of insertions and deletions at model positions with low insertion or deletion penalties is examined in hopes of speeding up convergence. The performance of the biased and unbiased mutation operators is compared. Both biased and unbiased genetic algorithms are also compared to a steepest-descent algorithm, which is a comparison lacking in prior work.

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This document was originally published by IEEE in the Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. Copyright restrictions may apply.