Autoencoder architecture optimization by genetic programming.

Photo by Sander Weeteling on Unsplash

One of the main challenges in the development of neural networks is to determine the architecture. That means how the different layers are connected, the depth, the units in each layer, and the activation for each layer. In the following post, I will show a method to optimize the architecture of an autoencoder by genetic programming.