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We propose DoE2Vec, a variational autoencoder (VAE)-based methodology tolearn optimization landscape characteristics for downstream meta-learningtasks, e.g., automated selection of optimization algorithms. Principally, usinglarge training data sets generated with a random function generator, DoE2Vecself-learns an informative latent representation for any design of experiments(DoE). Unlike the classical exploratory landscape analysis (ELA) method, ourapproach does not require any feature engineering and is easily applicable forhigh dimensional search spaces. For validation, we inspect the quality oflatent reconstructions and analyze the latent representations using differentexperiments. The latent representations not only show promising potentials inidentifying similar (cheap-to-evaluate) surrogate functions, but also cansignificantly boost performances when being used complementary to the classicalELA features in classification tasks.

DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis