  The performance of Convolutional Neural Networks (CNNs) highly relies on
their architectures. In order to design a CNN with promising performance,
extended expertise in both CNNs and the investigated problem is required, which
is not necessarily held by every user interested in CNNs or the problem domain.
In this paper, we propose to automatically evolve CNN architectures by using a
genetic algorithm based on ResNet blocks and DenseNet blocks. The proposed
algorithm is \textbf{completely} automatic in designing CNN architectures,
particularly, neither pre-processing before it starts nor post-processing on
the designed CNN is needed. Furthermore, the proposed algorithm does not
require users with domain knowledge on CNNs, the investigated problem or even
genetic algorithms. The proposed algorithm is evaluated on CIFAR10 and CIFAR100
against 18 state-of-the-art peer competitors. Experimental results show that it
outperforms state-of-the-art CNNs hand-crafted and CNNs designed by automatic
peer competitors in terms of the classification accuracy, and achieves the
competitive classification accuracy against semi-automatic peer competitors. In
addition, the proposed algorithm consumes much less time than most peer
competitors in finding the best CNN architectures.
