Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)

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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)

Pardalos, Panos M. (EDT)/ Rasskazova, Varvara (EDT)/ Vrahatis, Michael N. (EDT)

ウェブストア価格 ¥29,170（本体¥26,519）
Springer Nature Switzerland AG（2021/06発売）
外貨定価 US$ 139.99
ポイント 265pt

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製本 Hardcover:ハードカバー版／ページ数 388 p.
商品コード 9783030665142

Full Description

This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.

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).