Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science
1. Auflage 2021
ISBN: 978-3-030-74642-1
Verlag: Springer International Publishing

Buch, Englisch, 393 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 633 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Jin, Yaochu

Sun, Chaoli

Wang, Handing

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

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.