Population Historical Information-Driven Evolutionary Multitask Neural Architecture Search.

Abstract

Neural architecture search (NAS) has achieved significant success in automating neural network design, particularly through evolutionary NAS. To address the critical need for efficient architecture discovery across diverse scenarios, such as computer vision and natural language processing, multitask NAS (MT-NAS) methods have emerged. Nevertheless, existing MT-NAS approaches still face critical challenges, including redundant search arising from insufficient exploitation of population historical information across generations and negative transfer caused by unguided interactions between tasks. To address these limitations, a population historical information-driven evolutionary multitask neural architecture search (HIMT-NAS) algorithm is proposed. For each generation, the population historical information is recorded, which includes the operation information and the topology information. In the search process, systematic utilization of population historical information to guide evolutionary search directions, preventing redundant search. Furthermore, the proposed method adjusts cross-task knowledge transfer probability by measuring task similarity through patterns in population historical information, and then updates transfer probabilities when the information proves useful across multiple tasks. Extensive experiments on MedMNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate consistent advantages of the proposed method over both single-task NAS methods and recent MT-NAS methods.