论文标题
免疫网络：一种用于搜索卷积神经网络体系结构的免疫网络方法
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures
论文作者
论文摘要
在这项研究中，我们提出了免疫网络理论启发的新型神经架构搜索（NAS）方法。免疫NAS的核心建立在原始的免疫网络算法上，该算法迭代地通过超名和选择来更新人群，并消除了通过比较抗体亲和力和特异性相似性来不满足要求的自我生成个体。此外，为了促进突变操作，我们提出了一种新型的基于两部分的神经结构编码策略。此外，根据这种编码方法提出了基于标准遗传算法（SGA）的改进的突变策略。最后，基于提出的基于两组分的编码方法，开发了一种新的抗体亲和力计算方法来筛选合适的神经体系结构。系统评估表明，我们的系统在MNIST和CIFAR-10数据集上都取得了良好的性能。我们在Github上开放代码，以便与其他深入学习的研究人员和从业人员分享。
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.
