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.

翻译：我们提出ECToNAS，一种无需任何预训练元控制器的低成本进化跨拓扑神经架构搜索算法。该框架能够为不同任务和超参数设置自动选择适配的网络架构，并在需要时独立执行跨拓扑优化。作为一种混合方法，它将训练与拓扑优化融合为一个轻量级、资源高效的统一流程。我们通过六个标准数据集（CIFAR-10、CIFAR-100、EuroSAT、Fashion MNIST、MNIST、SVHN）验证了该方法的有效性与强大能力，展示了算法不仅能优化同一架构类型内的拓扑结构，还能在需要时动态增删卷积细胞，从而跨越不同网络类型间的边界。这使得不具备机器学习背景的研究者能够利用计算成本低廉、易于使用的跨拓扑神经架构搜索框架（将拓扑优化完整封装于训练过程中）在其领域内选用合适的模型类型与拓扑结构，并应用机器学习方法。