Genetic algorithms, inspired by natural selection and genetics, employ evolutionary principles such as selection, crossover, and mutation to evolve solutions over generations. Reinforcement learning, on the other hand, focuses on learning optimal decision-making policies through interaction with an environment, guided by reward signals. This paper provides an overview of the integration of genetic algorithms and reinforcement learning, highlighting the benefits associated with their combination. Lately, researchers have combined reinforcement learning and genetic algorithms to improve the optimization process. Reinforcement learning agents can be used to choose genetic algorithm operators or tune hyperparameters, enhancing the performance of large-scale problems. A genetic algorithm can also be used to optimize the set of agents for reinforcement learning tasks. The integration of genetic algorithms and reinforcement learning represents a synergy of two powerful paradigms, each offering distinct strengths in problem-solving methodologies. This hybrid approach has the potential to address complex optimization problems and enhance learning systems.