Abstract
Grow and graft genetic programming (GGGP) evolves more than 50000 parameters in a state-of-the-art C program to make functional source code changes which give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed it also does better than parameters recommended by Andronescu, M., et al.: Bioinformatics 23(13) (2007) i19–i28.
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Notes
- 1.
The ViennaRNA package must first be configured with ./configure –enable-sse. https://www.tbi.univie.ac.at/RNA/documentation.html.
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I am grateful for the assistance of Rhiju Das and Fernando Portela, and our anonymous reviewers.
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Langdon, W.B., Petke, J., Lorenz, R. (2018). Evolving Better RNAfold Structure Prediction. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_14
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