Data-Driven Evolutionary Optimization

Yaochu Jin

ISBN 10: 3030746429 / ISBN 13: 9783030746421

Published by Springer, Palgrave Macmillan Jun 2022, 2022

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 420 pp. Englisch.

Seller Inventory # 9783030746421

Bibliographic Details

• Title: Data-Driven Evolutionary Optimization
• Publisher: Springer, Palgrave Macmillan Jun 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 3030746429
• ISBN 13: 9783030746421
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 23D millimeters 235 millimeters width by 155 millimeters height by 23 millimeters depth
• Weight: 633 grams

About this title

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.