Research in evolutionary algorithms for routing in Mobile Ad Hoc Networks (MANETs) focuses on leveraging bio-inspired optimization techniques to enhance route discovery, energy efficiency, and network reliability. Recent works employ genetic algorithms, particle swarm optimization, ant colony optimization, and differential evolution to identify optimal routing paths under dynamic topology changes and limited resources. Hybrid models that combine evolutionary algorithms with machine learning or fuzzy logic are also introduced to improve convergence speed and adaptability. These approaches effectively minimize routing overhead, balance energy consumption, and enhance packet delivery ratios. Overall, evolutionary algorithm–based routing provides a flexible and intelligent framework for optimizing performance and scalability in MANET environments.