  Recent years have witnessed the breakthrough success of deep convolutional
neural networks (DCNNs) in image classification and other vision applications.
Although freeing users from the troublesome handcrafted feature extraction by
providing a uniform feature extraction-classification framework, DCNNs still
require a handcrafted design of their architectures. In this paper, we propose
the genetic DCNN designer, an autonomous learning algorithm can generate a DCNN
architecture automatically based on the data available for a specific image
classification problem. We first partition a DCNN into multiple stacked meta
convolutional blocks and fully connected blocks, each containing the operations
of convolution, pooling, fully connection, batch normalization, activation and
drop out, and thus convert the architecture into an integer vector. Then, we
use refined evolutionary operations, including selection, mutation and
crossover to evolve a population of DCNN architectures. Our results on the
MNIST, Fashion-MNIST, EMNISTDigit, EMNIST-Letter, CIFAR10 and CIFAR100 datasets
suggest that the proposed genetic DCNN designer is able to produce
automatically DCNN architectures, whose performance is comparable to, if not
better than, that of stateof- the-art DCNN models
