Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science
 
 
Springer (Verlag)
  • erscheint ca. am 7. Juli 2021
 
  • Buch
  • |
  • Hardcover
  • |
  • IV, 296 Seiten
978-3-030-74639-1 (ISBN)
 

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.

1st ed. 2021
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • Für Beruf und Forschung
IV, 296 p.
  • Höhe: 23.5 cm
  • |
  • Breite: 15.5 cm
978-3-030-74639-1 (9783030746391)
10.1007/978-3-030-74640-7
weitere Ausgaben werden ermittelt
Introduction to Optimization.- Classical Optimization Algorithms.- Evolutionary and Swarm Optimization.- Introduction to Machine Learning.- Data-Driven Surrogate-Assisted Evolutionary Optimization.- Multi-Surrogate-Assisted Single-Objective Optimization.- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
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.
DNB DDC Sachgruppen

Noch nicht erschienen

139,09 €
inkl. 7% MwSt.
Vorbestellen