Evolution Strategy based Evolutionary Algorithm for RNA Secondary Structure Prediction

* The RNA secondary structure prediction is significant in many scientific and engineering fields. Because the solution space increases exponentially with the RNA sequence length, existing RNA secondary structure prediction approaches still have some limitations to predict the secondary structure accurately. In this paper, a novel evolution strategy is designed to solve the RNA secondary structure prediction problem. First, the consecutive base pairs set is calculated, which can provide stable negative free energy. Second, the consecutive base pairs are selected randomly from the obtained set, and the decoding order is determined by random permutation. Thirdly, the evolution strategy is adopted to search for the optimal solution with maximum base pair matching. Finally, the comparison results show competition performance over some chosen state-of-the-art approaches on Precision, Recall and F-measure indicators.