Genetic Algorithm-Based Solvers for RNA-RNA Interaction

RNA-RNA interactions (RRIs) are fundamental to a wide range of cellular processes, including gene regulation, mRNA stabilization, and viral replication. Computational prediction of RRIs has traditionally relied on dynamic programming-based approaches grounded in thermodynamic models, which, although accurate, are computationally intensive and face scalability limitations when applied to large or complex RNA sequences. To address these challenges, recent research has explored alternative paradigms based on metaheuristic optimization, particularly Genetic Algorithms (GAs), which reformulate RRI prediction as an optimization problem and enable efficient exploration of large and complex search spaces. Existing studies demonstrate that GA-based methods encode RNA interaction patterns as candidate solutions and iteratively refine them using biologically inspired operations such as selection, crossover, and mutation, while integrating thermodynamic and structural constraints into fitness functions. Comparative analyses in the literature indicate that these approaches can achieve competitive accuracy with traditional methods while offering improved flexibility and scalability for large-scale and heuristic-driven RRI prediction tasks.