Convolutional Neural Networks (CNNs) continue to achieve great success in classification tasks as innovative techniques and complex multi-path architecture topologies are introduced. Neural Architecture Search (NAS) aims to automate the design of these complex architectures, reducing the need for costly manual design work by human experts. Cellular Encoding (CE) is an evolutionary computation technique which excels in constructing novel multi-path topologies of varying complexity and has recently been applied with NAS to evolve CNN architectures for various classification tasks. However, existing CE approaches have severe limitations. They are restricted to only one domain, only partially implement the theme of CE, or only focus on the micro-architecture search space. This paper introduces a new CE representation and algorithm capable of evolving novel multi-path CNN architectures of varying depth, width, and complexity for image and text classification tasks. The algorithm explicitly focuses on the macro-architecture search space. Furthermore, by using a surrogate model approach, we show that the algorithm can evolve a performant CNN architecture in less than one GPU day, thereby allowing a sufficient number of experiment runs to be conducted to achieve scientific robustness. Experiment results show that the approach is highly competitive, defeating several state-of-the-art methods, and is generalisable to both the image and text domains.

翻译：卷积神经网络（CNN）随着创新技术与复杂多路径架构拓扑结构的引入，在分类任务中持续取得显著成功。神经架构搜索（NAS）旨在自动化设计这些复杂架构，减少人类专家昂贵的人工设计工作。细胞编码（CE）是一种擅长构建不同复杂度新型多路径拓扑结构的进化计算技术，近年来已被应用于NAS领域，用于进化适用于各类分类任务的CNN架构。然而，现有CE方法存在严重局限性：它们仅适用于单一领域，仅部分实现CE主题思想，或仅聚焦于微观架构搜索空间。本文提出一种新型CE表示方法与算法，能够针对图像和文本分类任务，进化出具有不同深度、宽度和复杂度的新型多路径CNN架构。该算法明确聚焦于宏观架构搜索空间。此外，通过采用代理模型方法，我们证明该算法能够在不足一个GPU天内进化出高性能CNN架构，从而允许开展足够数量的实验运行以实现科学稳健性。实验结果表明，该方法具有高度竞争力，击败了多项最先进方法，并可推广至图像与文本两个领域。