In this work, we propose a novel evolutionary algorithm for neural architecture search, applicable to global search spaces. The algorithm's architectural representation organizes the topology in multiple hierarchical modules, while the design process exploits this representation, in order to explore the search space. We also employ a curation system, which promotes the utilization of well performing sub-structures to subsequent generations. We apply our method to Fashion-MNIST and NAS-Bench101, achieving accuracies of $93.2\%$ and $94.8\%$ respectively in a relatively small number of generations.

翻译：本文提出了一种适用于全局搜索空间的新型进化算法，用于神经架构搜索。该算法的架构表示将拓扑结构组织成多个层次化模块，同时设计过程利用这种表示来探索搜索空间。我们还引入了一种精选系统，以促进性能优异的子结构在后续世代中的应用。我们将该方法应用于Fashion-MNIST和NAS-Bench101数据集，在相对较少的世代数中分别达到了$93.2\%$和$94.8\%$的准确率。