  The Zone of Avoidance makes it difficult for astronomers to catalogue
galaxies at low latitudes to our galactic plane due to high star densities and
extinction. However, having a complete sky map of galaxies is important in a
number of fields of research in astronomy. There are many unclassified sources
of light in the Zone of Avoidance and it is therefore important that there
exists an accurate automated system to identify and classify galaxies in this
region. This study aims to evaluate the efficiency and accuracy of using an
evolutionary algorithm to evolve the topology and configuration of
Convolutional Neural Network (CNNs) to automatically identify galaxies in the
Zone of Avoidance. A supervised learning method is used with data containing
near-infrared images. Input image resolution and number of near-infrared
passbands needed by the evolutionary algorithm is also analyzed while the
accuracy of the best evolved CNN is compared to other CNN variants.
