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Data-Driven Evolutionary Optimization

  • Fester Einband
  • 420 Seiten
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine le... Weiterlesen
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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.

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

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.


Titel: Data-Driven Evolutionary Optimization
Untertitel: Integrating Evolutionary Computation, Machine Learning and Data Science
EAN: 9783030746391
ISBN: 3030746399
Format: Fester Einband
Herausgeber: Springer International Publishing
Genre: Allgemeines & Lexika
Anzahl Seiten: 420
Gewicht: 793g
Größe: H241mm x B160mm x T28mm
Jahr: 2021
Untertitel: Englisch
Auflage: 1st ed. 2021

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