RNA Secondary Structure Prediction by Optimization Technique: Genetic Algorithm

RNA Secondary Structure prediction is one of the most significant research areas in bioinformatics. This paper presents a RNA secondary structure prediction using the permutation-based Genetic Algorithm (GA). We analyse the selection function STDS and Keep-Best Reproduction (KBR) genetic algorithm replacement techniques, and also using the genetic algorithm crossover operators. We take the SsrS RNA sequences which were the first non-coding RNA to be sequenced, it having the large nucleotides (184nt) that fold into an extended hairpin structure with a large single-stranded internal bulge. We find out the lowest free energy in the individuals RNA sequences with help of the fitness function and which one individual sequence having lowest free energy that individual sequence will be predict the best optimization secondary structure in the RNA individualsequences. Keywords— Genetic algorithm, RNA secondary structure prediction, Genetic Algorithm representation, and Genetic Algorithm crossover operators.