We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e.g., automated selection of optimization algorithms. Principally, using large training data sets generated with a random function generator, DoE2Vec self-learns an informative latent representation for any design of experiments (DoE). Unlike the classical exploratory landscape analysis (ELA) method, our approach does not require any feature engineering and is easily applicable for high dimensional search spaces. For validation, we inspect the quality of latent reconstructions and analyze the latent representations using different experiments. The latent representations not only show promising potentials in identifying similar (cheap-to-evaluate) surrogate functions, but also can significantly boost performances when being used complementary to the classical ELA features in classification tasks.

翻译：我们提出了DoE2Vec，一种基于变分自编码器（VAE）的方法，用于学习用于下游元学习任务（例如自动选择优化算法）的优化景观特征。主要使用通过随机函数生成器生成的大型训练数据集，DoE2Vec自学习任何实验设计（DoE）的信息潜在表述。与经典的探索性景观分析（ELA）方法不同，我们的方法不需要任何特征工程，在高维搜索空间中容易适用。为了验证，我们检查潜在重建的质量，并使用不同的实验分析潜在表述。潜在表述不仅在识别相似的（昂贵的评估）代理函数方面显示出有前途的潜力，而且当作为与经典ELA特征互补使用时，也可以显着提高分类任务的性能。