Figure 2

Overview of RNAEAPath. Overview of RNAEAPath. In this procedure, the input is an RNA sequence x with the start and end structures A and B, and the output is the best folding pathway in k iterations (OPT _k ). For notations, k is the number of iterations, ℙ₀ is the initial population, and ℙ_k is the folding pathway population of the k^th iteration. $\mathbb{T}$ contains all the offspring folding pathways produced by applying mutation strategies ${\mathbb{M}}_1,\dots ,{\mathbb{M}}_Y$ to each pathway p in the (k - 1)^st population. ${\mathbb{O}}_k$ is an ordered list of offspring folding pathways of the k^th generation, from which, the population for the next iteration ( ℙ _{k +1}) is selected. Folding pathways in ℙ _k and ${\mathbb{O}}_k$ are sorted based on their fitness and ℙ _k [1 . . . ℓ] are the top ℓ best folding pathways in ℙ _k .