  Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for
the processing of images. However the configuration and training of these
networks is a complex task requiring deep domain knowledge, experience and much
trial and error. Using genetic algorithms, competitive CNN topologies for image
recognition can be produced for any specific purpose, however in previous work
this has come at high computational cost. In this work two novel approaches are
presented to the utilisation of these algorithms, effective in reducing
complexity and training time by nearly 20%. This is accomplished via
regularisation directly on training time, and the use of partial training to
enable early ranking of individual architectures. Both approaches are validated
on the benchmark CIFAR10 data set, and maintain accuracy.
