DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis

Understand

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.