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Black box optimization, machine learning, and no-free lunch theorems

Pardalos, Panos M.(Edited by)Rasskazova, Varvara(Edited by)Vrahatis, Michael N.(Edited by)
Part of the Springer Optimization and Its Applications series
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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.

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Product Details
3030665178 / 9783030665173
Paperback / softback
006.31
28/06/2022
Switzerland
English
388 pages : illustrations (black and white, and colour)
24 cm