  In recent years, convolutional neural networks (CNNs) have become deeper in
order to achieve better classification accuracy in image classification.
However, it is difficult to deploy the state-of-the-art deep CNNs for
industrial use due to the difficulty of manually fine-tuning the
hyperparameters and the trade-off between classification accuracy and
computational cost. This paper proposes a novel multi-objective optimization
method for evolving state-of-the-art deep CNNs in real-life applications, which
automatically evolves the non-dominant solutions at the Pareto front. Three
major contributions are made: Firstly, a new encoding strategy is designed to
encode one of the best state-of-the-art CNNs; With the classification accuracy
and the number of floating point operations as the two objectives, a
multi-objective particle swarm optimization method is developed to evolve the
non-dominant solutions; Last but not least, a new infrastructure is designed to
boost the experiments by concurrently running the experiments on multiple GPUs
across multiple machines, and a Python library is developed and released to
manage the infrastructure. The experimental results demonstrate that the
non-dominant solutions found by the proposed algorithm form a clear Pareto
front, and the proposed infrastructure is able to almost linearly reduce the
running time.
