Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. In this work, we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.

深度学习在自动化特征工程过程方面非常成功，广泛应用于各种任务，如语音识别、分类、图像分割、时间序列预测等。深度神经网络 (DNN) 结合了通过数据学习模式的能力，遵循端到端的方式并扩展了在现实世界问题中的适用性，因为需要较少的预处理。随着规模和复杂性的快速增长，DNN 的设计和配置出现了新的挑战。在这项工作中，我们提出了一项关于将基于进化语法的遗传编程算法 (GP) 作为 DNN 设计的统一方法的研究。随着对神经进化的研究越来越多，进化方法在该主题中越来越受欢迎。我们在三个不同的应用中验证了我们的方法：用于图像分类的卷积神经网络设计、用于文本分类的图神经网络和用于图像分割的 U-Nets。结果表明，基于进化语法的 GP 可以有效地生成不同的 DNN 架构，以适应每个问题，采用与手工设计的网络中通常看到的不同的选择。这种方法在架构设计方面显示出很多希望，可以与同行取得竞争结果。适应每个问题，采用不同于通常在手工设计的网络中看到的选择。这种方法在架构设计方面显示出很多希望，可以与同行取得竞争结果。适应每个问题，采用不同于通常在手工设计的网络中看到的选择。这种方法在架构设计方面显示出很多希望，可以与同行取得竞争结果。