Deep evolution of image representations for handwritten digit recognition
Alexandros Agapitos, Michael O’Neill, Miguel Nicolau, David S. Fagan, Ahmed Kattan, Anthony Brabazon, Kathleen M. Curran · 2015
A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.