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Deep Convolutional Neural Networks (CNNs) have been widely used in imageclassification tasks, but the process of designing CNN architectures is verycomplex, so Neural Architecture Search (NAS), automatically searching foroptimal CNN architectures, has attracted more and more research interests.However, the computational cost of NAS is often too high to apply NAS onreal-life applications. In this paper, an efficient particle swarm optimisationmethod named EPSOCNN is proposed to evolve CNN architectures inspired by theidea of transfer learning. EPSOCNN successfully reduces the computation cost byminimising the search space to a single block and utilising a small subset ofthe training set to evaluate CNNs during evolutionary process. Meanwhile,EPSOCNN also keeps very competitive classification accuracy by stacking theevolved block multiple times to fit the whole dataset. The proposed EPSOCNNalgorithm is evaluated on CIFAR-10 dataset and compared with 13 peercompetitors comprised of deep CNNs crafted by hand, learned by reinforcementlearning methods and evolved by evolutionary computation approaches, whichshows very promising results by outperforming all of the peer competitors withregard to the classification accuracy, number of parameters and thecomputational cost.

Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks