Titlebook: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems; Panos M. Pardalos,Varvara Rasskazova,Michael N. Vr Book 2021 Springe

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Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics,ake this future state better. In some practical situations, we know how the state changes with time—e.g., in meteorology, we know the partial differential equations that describe the atmospheric processes. In such situations, prediction becomes a purely computational problem. In many other situation

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Non-lattice Covering and Quantization of High Dimensional Sets,ted values of . are between 5 and 50; . can be in hundreds or thousands and the designs (collections of points) are nested. This paper is a continuation of our paper (Noonan and Zhigljavsky, SN Oper Res Forum, 2020), where we have theoretically investigated several simple schemes and numerically stu

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Finding Effective SAT Partitionings Via Black-Box Optimization,ariables of an original formula to partition it into a family of subproblems that are significantly easier to solve individually. While it is usually very hard to estimate the time required to solve a hard SAT instance without actually solving it, the partitionings of the presented kind make it poss

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The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?, probably the fundamental theoretical result of the Machine Learning field but its practical meaning and implication for practitioners facing “real life” industrial and design optimization problems are rarely addressed in the technical literature. This discussion is intended for a broad audience of

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Die technische Torsionstheorie,de a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning,

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