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

Jin, Yaochu; Wang, Handing; Sun, Chaoli

ISBN 10: 3030746429 / ISBN 13: 9783030746421

Edité par Springer, 2022

In. N° de réf. du vendeur ria9783030746421_new

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 bibliographiques

Titre : Data-Driven Evolutionary Optimization: ...
Éditeur : Springer
Date d'édition : 2022
Reliure : Couverture souple
Etat : New