Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science - Couverture rigide

Jin, Yaochu; Wang, Handing; Sun, Chaoli

Couverture rigide

ISBN 10 : 3030746399 ISBN 13 : 9783030746391

Editeur : Springer Nature Switzerland AG, 2021

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.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2021
• Langue: anglais
• ISBN 10: 3030746399
• ISBN 13: 9783030746391
• Reliure: Relié
• Nombre de pages: 418
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne