  Convolutional neural networks belong to the most successul image classifiers,
but the adaptation of their network architecture to a particular problem is
computationally expensive. We show that an evolutionary algorithm saves
training time during the network architecture optimization, if learned network
weights are inherited over generations by Lamarckian evolution. Experiments on
typical image datasets show similar or significantly better test accuracies and
improved convergence speeds compared to two different baselines without weight
inheritance. On CIFAR-10 and CIFAR-100 a 75 % improvement in data efficiency is
observed.
