RNA Secondary Structure Prediction Using a Genetic Algorithm with a Selection Method Based on Free Energy Value and Topological Index

This paper presents a genetic algorithm designed to predict RNA secondary structures, which utilizes selection criteria based on free energy (fitness) and topological similarity. This approach represents structural information using a simple number, facilitating comparisons between foldings. The simplified graph representation identifies similarities between structures that have the same type of branches. The results demonstrate that the algorithm identifies the final secondary structure with the same level of precision as the commonly used dynamic programming, but with the advantage of producing more optimal structures with different topologies. This approach maintains high population diversity and allows for the exploration of many suboptimal structures in parallel, avoiding the possibility of getting stuck in a local minimum. This permits the investigation of not only the structure with the minimum free energy, but also of other low-energy structures with different topologies that are closer to the natural fold.