1 research outputs found
Evolution of Convolutional Highway Networks
Convolutional highways are deep networks based on multiple stacked
convolutional layers for feature preprocessing. We introduce an evolutionary
algorithm (EA) for optimization of the structure and hyperparameters of
convolutional highways and demonstrate the potential of this optimization
setting on the well-known MNIST data set. The (1+1)-EA employs Rechenberg's
mutation rate control and a niching mechanism to overcome local optima adapts
the optimization approach. An experimental study shows that the EA is capable
of improving the state-of-the-art network contribution and of evolving highway
networks from scratch.Comment: 8 pages, 4 figure