http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Black box optimization, machine learning, and no-free lunch theorems
Learning enabled constrained black box optimization (Archetti) -- Black-box optimization: Methods and applications (Hasan) -- Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein) -- Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis) -- Multi-objective evolutionary algorithms: Past, present and future (Coello C.A) -- Black-box and data driven computation (Du) -- Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott) -- Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich) -- Variable neighborhood programming as a tool of machine learning (Mladenovic) -- Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky) -- Finding effective SAT partitionings via black-box optimization (Semenov) -- The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino) -- What is important about the No Free Lunch theorems? (Wolpert).
Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors.
Springer optimization and its applications ; v. 170