  Deep Convolutional Neural Networks (CNNs) have been widely used in image
classification tasks, but the process of designing CNN architectures is very
complex, so Neural Architecture Search (NAS), automatically searching for
optimal CNN architectures, has attracted more and more research interests.
However, the computational cost of NAS is often too high to apply NAS on
real-life applications. In this paper, an efficient particle swarm optimisation
method named EPSOCNN is proposed to evolve CNN architectures inspired by the
idea of transfer learning. EPSOCNN successfully reduces the computation cost by
minimising the search space to a single block and utilising a small subset of
the training set to evaluate CNNs during evolutionary process. Meanwhile,
EPSOCNN also keeps very competitive classification accuracy by stacking the
evolved block multiple times to fit the whole dataset. The proposed EPSOCNN
algorithm is evaluated on CIFAR-10 dataset and compared with 13 peer
competitors comprised of deep CNNs crafted by hand, learned by reinforcement
learning methods and evolved by evolutionary computation approaches, which
shows very promising results by outperforming all of the peer competitors with
regard to the classification accuracy, number of parameters and the
computational cost.
