Architecture Optimization of Deep Learning Networks

This chapter describes how genetic algorithms can be used to improve the performance of artificial neural network-based models by optimizing the network architecture of these models. We will start with a brief introduction to neural networks and deep learning. After introducing the Iris dataset and the Multilayer Perceptron classifier, we will demonstrate network architecture optimization using a genetic algorithm-based solution. Then, we will extend this approach to combine network architecture optimization with model hyperparameter tuning, which will be jointly carried out by a genetic algorithm-based solution.

In this chapter, we will cover the following topics:

• Understanding the basic concepts of artificial neural networks and deep learning
• The Iris dataset and the Multilayer Perceptron (MLP) classifier
• Enhancing the performance...