Convolutional Neural Networks (CNNs) are state-of-the-art algorithms for image recognition. To configure a CNN properly by hand, one requires deep domain knowledge, best practises, and trial and error. As deep learning progresses, manual configuration gets harder and automated ways such as genetic algorithms are required. We describe in-depth how CNN topologies can be evolved; to do so the "half pixel problem" that occurs during programmatic CNN creation and manipulation is established, analysed and solved. A new human-readable genome representation for topologies and a novel ancestry tree visualisation for genetic algorithms is used to deepen understanding of the algorithm. We rediscover common design patterns within the topologies found and see when and how the algorithm can recover from wrong assumptions in its initialisation logic. Regularisation and partial training is introduced, allowing speed-ups of up to 19% while maintaining result accuracy.