We present ECToNAS, a cost-efficient evolutionary cross-topology neural
architecture search algorithm that does not require any pre-trained meta
controllers. Our framework is able to select suitable network architectures for
different tasks and hyperparameter settings, independently performing
cross-topology optimisation where required. It is a hybrid approach that fuses
training and topology optimisation together into one lightweight,
resource-friendly process. We demonstrate the validity and power of this
approach with six standard data sets (CIFAR-10, CIFAR-100, EuroSAT, Fashion
MNIST, MNIST, SVHN), showcasing the algorithm's ability to not only optimise
the topology within an architectural type, but also to dynamically add and
remove convolutional cells when and where required, thus crossing boundaries
between different network types. This enables researchers without a background
in machine learning to make use of appropriate model types and topologies and
to apply machine learning methods in their domains, with a computationally
cheap, easy-to-use cross-topology neural architecture search framework that
fully encapsulates the topology optimisation within the training process.

ECToNAS 是一种成本效益高的进化交叉拓扑神经架构搜索算法，它不需要任何预训练元控制器。该框架能够为不同的任务和超参数设置选择适当的网络架构，在需要时独立进行交叉拓扑优化。这是一种将训练和拓扑优化融合为一个轻量级、资源友好的过程的混合方法。我们通过六个标准数据集（CIFAR-10，CIFAR-100，EuroSAT，Fashion MNIST，MNIST，SVHN）展示了这种方法的有效性和能力，展示了该算法不仅能够优化同一种架构类型内的拓扑，还能够在需要的时候动态添加和删除卷积单元，从而跨越不同网络类型的边界。这使得没有机器学习背景的研究人员能够利用合适的模型类型和拓扑，在其领域应用机器学习方法，使用一种计算成本低、易于使用的交叉拓扑神经架构搜索框架，完全将拓扑优化嵌入到训练过程中。