Architecture Optimization of Deep Learning Networks
This chapter describes how genetic algorithms can be used to improve the performance of artificial neural network (ANN)-based models by optimizing the network architecture of these models. We will start with a brief introduction to neural networks (NNs) and deep learning (DL). After introducing the Iris dataset and Multilayer Perceptron (MLP) classifiers, 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 ANNs and DL
- Enhancing the performance of a DL classifier using network architecture optimization
- Further enhancing the performance of the DL classifier by combining network architecture optimization...
