  In this study, we build upon a previously proposed neuroevolution framework
to evolve deep convolutional models. Specifically, the genome encoding and the
crossover operator are extended to make them applicable to layered networks. We
also propose a convolutional layer layout which allows kernels of different
shapes and sizes to coexist within the same layer, and present an argument as
to why this may be beneficial. The proposed layout enables the size and shape
of individual kernels within a convolutional layer to be evolved with a
corresponding new mutation operator. The proposed framework employs a hybrid
optimisation strategy involving structural changes through epigenetic evolution
and weight update through backpropagation in a population-based setting.
Experiments on several image classification benchmarks demonstrate that the
crossover operator is sufficiently robust to produce increasingly performant
offspring even when the parents are trained on only a small random subset of
the training dataset in each epoch, thus providing direct confirmation that
learned features and behaviour can be successfully transferred from parent
networks to offspring in the next generation.
