The article explores the integration of genetic algorithms (GAs) and neural networks (NNs) using the PyGAD Python library to build and train both classification and regression neural networks. It highlights the biologically-inspired nature of both GAs and NNs, emphasizing their potential synergy in creating a hybrid approach for high-accuracy training. PyGAD's gann module facilitates the creation of a neural network population, while the pygad module applies the genetic algorithm to optimize the network's parameters over multiple generations. By iterating through this evolutionary process, the network's weights are updated to improve prediction accuracy, as demonstrated through examples like solving the XOR logic gate problem. Although the process can be time-consuming, particularly with complex issues, the tutorial suggests that using tools like Cython might enhance PyGAD's performance.