  In this research, we propose ImmuNetNAS, a novel Neural Architecture Search
(NAS) approach inspired by the immune network theory. The core of ImmuNetNAS is
built on the original immune network algorithm, which iteratively updates the
population through hypermutation and selection, and eliminates the
self-generation individuals that do not meet the requirements through comparing
antibody affinity and inter-specific similarity. In addition, in order to
facilitate the mutation operation, we propose a novel two-component based
neural structure coding strategy. Furthermore, an improved mutation strategy
based on Standard Genetic Algorithm (SGA) was proposed according to this
encoding method. Finally, based on the proposed two-component based coding
method, a new antibody affinity calculation method was developed to screen
suitable neural architectures. Systematic evaluations demonstrate that our
system has achieved good performance on both the MNIST and CIFAR-10 datasets.
We open-source our code on GitHub in order to share it with other deep learning
researchers and practitioners.
