Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975)

Jin, Yaochu/ Wang, Handing/ Sun, Chaoli

ISBN 10: 3030746399 / ISBN 13: 9783030746391

Edité par Springer, 2021

A propos de cet article

418 pages. 9.25x6.10x1.02 inches. In Stock.

N° de réf. du vendeur x-3030746399

Détails bibliographiques

• Titre: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975)
• Éditeur: Springer
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030746399
• ISBN à 13 chiffres: 9783030746391
• Reliure: Hardcover
• État de l'article: Brand New

A propos de ce titre

Synopsis

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.

This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.