  Deploying models across diverse devices demands tradeoffs among multiple
objectives due to different resource constraints. Arguably, due to the small
model trap problem in multi-objective neural architecture search (MO-NAS) based
on a supernet, existing approaches may fail to maintain large models. Moreover,
multi-tasking neural architecture search (MT-NAS) excels in handling multiple
tasks simultaneously, but most existing efforts focus on tasks from the same
dataset, limiting their practicality in real-world scenarios where multiple
tasks may come from distinct datasets. To tackle the above challenges, we
propose a Multi-Objective Evolutionary Multi-Tasking framework for NAS
(MO-EMT-NAS) to achieve architectural knowledge transfer across tasks from
different datasets while finding Pareto optimal architectures for
multi-objectives, model accuracy and computational efficiency. To alleviate the
small model trap issue, we introduce an auxiliary objective that helps maintain
multiple larger models of similar accuracy. Moreover, the computational
efficiency is further enhanced by parallelizing the training and validation of
the weight-sharing-based supernet. Experimental results on seven datasets with
two, three, and four task combinations show that MO-EMT-NAS achieves a better
minimum classification error while being able to offer flexible trade-offs
between model performance and complexity, compared to the state-of-the-art
single-objective MT-NAS algorithms. The runtime of MO-EMT-NAS is reduced by
59.7% to 77.7%, compared to the corresponding multi-objective single-task
approaches.
