  Convolutional neural networks (CNNs) have constantly achieved better
performance over years by introducing more complex topology, and enlarging the
capacity towards deeper and wider CNNs. This makes the manual design of CNNs
extremely difficult, so the automated design of CNNs has come into the research
spotlight, which has obtained CNNs that outperform manually-designed CNNs.
However, the computational cost is still the bottleneck of automatically
designing CNNs. In this paper, inspired by transfer learning, a new
evolutionary computation based framework is proposed to efficiently evolve CNNs
without compromising the classification accuracy. The proposed framework
leverages multi-source domains, which are smaller datasets than the target
domain datasets, to evolve a generalised CNN block only once. And then, a new
stacking method is proposed to both widen and deepen the evolved block, and a
grid search method is proposed to find optimal stacking solutions. The
experimental results show the proposed method acquires good CNNs faster than 15
peer competitors within less than 40 GPU-hours. Regarding the classification
accuracy, the proposed method gains its strong competitiveness against the peer
competitors, which achieves the best error rates of 3.46%, 18.36% and 1.76% for
the CIFAR-10, CIFAR-100 and SVHN datasets, respectively.
