Langdon, WB;
Petke, J;
Lorenz, R;
(2018)
Evolving better RNAfold structure prediction.
In:
Genetic Programming. EuroGP 2018.
(pp. pp. 220-236).
Springer International Publishing: Cham, Switzerland.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Evolving better RNAfold structure prediction |
| Event: | EuroGP 2018, European Conference on Genetic Programming, 4-6 April 2018, Parma, Italy |
| ISBN-13: | 9783319775524 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-77553-1_14 |
| Publisher version: | https://doi.org/10.1007/978-3-319-77553-1_14 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | genetic improvement, genetic algorithms, genetic programming, software engineering, SBSE, software maintenance of empirical constants, Bioinformatics, local search, genomic and phenotypic Tabu restrictions, genetic repair |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10048826 |
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