Yaochu Jin, Handing Wang, Chaoli Sun

Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 975
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-74639-1
• 1st edition 2021
• Seitenzahl: 420
• Erscheinungstermin: 29. Juni 2021
• Englisch
• Abmessung: 241mm x 160mm x 29mm
• Gewicht: 793g
• ISBN-13: 9783030746391
• ISBN-10: 3030746399
• Artikelnr.: 61379730

Inhaltsangabe

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.