Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time.

中文翻译：

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EvoAAA：自动神经自动编码器体系结构搜索的进化方法

当向其提供精选功能时，机器学习模型会更好地工作。特征工程方法通常被用作获取或构建适当特征集的预处理步骤。近年来，自动编码器（一种特定类型的对称神经网络）已广泛用于执行表示学习，证明了它们与经典特征工程算法相比具有竞争力。使用自动编码器的主要障碍是找到一个好的架构，这是大多数专家手动面对的过程。提出了一种基于进化方法的自动编码器对称体系结构自动搜索程序。该方法论针对九个异构数据集进行了测试。获得的结果表明，这种方法能够找到更好的架构，