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Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: Studies in Computational Intelligence, cartea 975

Autor Yaochu Jin, Handing Wang, Chaoli Sun
en Limba Engleză Paperback – 30 iun 2022
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.
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Specificații

ISBN-13: 9783030746421
ISBN-10: 3030746429
Ilustrații: XXV, 393 p. 159 illus., 76 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.59 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Includes a brief introduction to mathematical programming, metaheuristic algorithms, and machine learning techniques Presents a systematic description of most recent research advances in data-driven evolutionary optimization, including surrogate-assisted single-, multi-, and many-objective optimization Introduces various intuitive and mathematical surrogate management strategies, such as the trust region method and acquisition functions in Bayesian optimization Provides applications of data-driven optimization to engineering design, automation of process industry, health care, and automated machine learning