In this chapter, you were introduced to the basic concepts of artificial neural networks and deep learning. After getting acquainted with the Iris dataset and the Multilayer Perceptron (MLP) classifier, you were presented with the notion of network architecture optimization. Next, we demonstrated a genetic algorithm-based optimization of network architecture for the MLP classifier. Finally, we were able to combine network architecture optimization with model hyperparameter tuning with the genetic algorithms process and enhance the performance of the classifier even further.
So far, we have concentrated on supervised learning. In the next chapter, we will look into applying genetic algorithms to reinforcement learning, an exciting and fast-developing branch of machine learning.