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Automated per-instance algorithm selection and configuration have shownpromising performances for a number of classic optimization problems, includingsatisfiability, AI planning, and TSP. The techniques often rely on a set offeatures that measure some characteristics of the problem instance at hand. Inthe context of black-box optimization, these features have to be derived from aset of $(x,f(x))$ samples. A number of different features have been proposed inthe literature, measuring, for example, the modality, the separability, or theruggedness of the instance at hand. Several of the commonly used features,however, are highly correlated. While state-of-the-art machine learningtechniques can routinely filter such correlations, they hinder explainabilityof the derived algorithm design techniques.  We therefore propose in this work to pre-process the measured (raw) landscapefeatures through representation learning. More precisely, we show that a lineardimensionality reduction via matrix factorization significantly contributestowards a better detection of correlation between different problem instances-- a key prerequisite for successful automated algorithm design.

Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes