Part 3: Artificial Intelligence Applications of Genetic Algorithms

This part focuses on using genetic algorithms to enhance various artificial intelligence tasks, including machine learning and natural language processing. It starts by showcasing how these algorithms can enhance supervised learning models through optimal feature selection for regression and classification tasks. We then explore the improvement of model performance through hyperparameter tuning, comparing traditional grid search methods with genetic algorithm approaches. We then shift our focus to the optimization of artificial neural network architectures, using the Iris dataset to illustrate the combined optimization of network structure and hyperparameters. In the realm of reinforcement learning, genetic algorithms are then applied to tackle Gymnasium’s MountainCar and CartPole challenges, while for natural language processing we see genetic algorithms in action solving a mystery word game and assisting in...