Survey details features for black-box optimization in machine learning

This survey paper details the features used to represent single-objective continuous black-box optimization problems. It covers problem landscape features, algorithm features, and interaction features, drawing on recent research from the past five years. The paper aims to support machine learning tasks like algorithm selection and configuration, and also evaluates benchmark problem set complementarity. Limitations and future research directions are also discussed. AI

IMPACT Provides a structured overview of feature engineering techniques relevant to machine learning in optimization problems.

RANK_REASON The item is a survey paper published on arXiv detailing features for machine learning applications in optimization. [lever_c_demoted from research: ic=1 ai=1.0]

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