Evolutionary Multitasking CNN Architecture Search for Hyperspectral Image Classification

In recent years, convolutional neural networks (CNNs) have shown excellent effectiveness on hyperspectral image classification (HSI) tasks. However, it is a challenge to design a suitable CNN architecture to obtain great performance according to different tasks. Different from the traditional manual design, in this paper, an evolutionary multitasking CNN architecture search framework for HSI classification is proposed to search the optimal architectures and accomplish classification of different tasks simultaneously. Through encoding the CNN architectures, the proposed algorithm is able to achieve global search in the same search space and select well-adapted individuals for evolution. In the evolutionary multitasking environment, information can be transferred between and within tasks, which can accelerate the convergence and explore good architectures through beneficial transfer. In the experiments, the effectiveness of the proposed method is demonstrated by the comparison with different methods on two common data sets.