  Recently, optical neural networks (ONNs) integrated in photonic chips has
received extensive attention because they are expected to implement the same
pattern recognition tasks in the electronic platforms with high efficiency and
low power consumption. However, the current lack of various learning algorithms
to train the ONNs obstructs their further development. In this article, we
propose a novel learning strategy based on neuroevolution to design and train
the ONNs. Two typical neuroevolution algorithms are used to determine the
hyper-parameters of the ONNs and to optimize the weights (phase shifters) in
the connections. In order to demonstrate the effectiveness of the training
algorithms, the trained ONNs are applied in the classification tasks for iris
plants dataset, wine recognition dataset and modulation formats recognition.
The calculated results exhibit that the training algorithms based on
neuroevolution are competitive with other traditional learning algorithms on
both accuracy and stability. Compared with previous works, we introduce an
efficient training method for the ONNs and demonstrate their broad application
prospects in pattern recognition, reinforcement learning and so on.
