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DoE2Vec，一种基于变分自编码器（VAE）的方法，用于学习优化景观特征，以支持下游元学习任务（例如优化算法的自动选择）。该技术主要通过随机函数生成器产生的大规模训练数据集，使DoE2Vec能够自主学习任何实验设计（DoE）的信息性潜在表示。与经典的探索性景观分析（ELA）方法不同，我们的方法无需任何特征工程，且易于应用于高维搜索空间。为验证有效性，我们通过不同实验检验潜在重构的质量并分析潜在表示。结果表明，潜在表示不仅在识别相似（低成本评估）代理函数方面展现出良好潜力，而且在分类任务中作为经典ELA特征的补充时，能够显著提升性能。