Adaptation, Learning, and Optimization

Recent Advances in Evolutionary Multi-objective Optimization

Editors: Bechikh, Slim, Datta, Rituparna, Gupta, Abhishek (Eds.)

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  • Provides both methodological treatments and real world insights
  • Serves as comprehensive reference for researchers, practitioners, and advanced-level students
  • Covers both the theory and practice of using evolutionary algorithms in tackling real world applications involving multiple objectives
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eBook $109.00
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  • ISBN 978-3-319-42978-6
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Hardcover $149.99
price for USA in USD
  • ISBN 978-3-319-42977-9
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  • Usually dispatched within 3 to 5 business days.
Softcover $149.99
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  • ISBN 978-3-319-82709-4
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About this book

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

Table of contents (6 chapters)

Table of contents (6 chapters)
  • Multi-objective Optimization: Classical and Evolutionary Approaches

    Pages 1-30

    Elarbi, Maha (et al.)

  • Dynamic Multi-objective Optimization Using Evolutionary Algorithms: A Survey

    Pages 31-70

    Azzouz, Radhia (et al.)

  • Evolutionary Bilevel Optimization: An Introduction and Recent Advances

    Pages 71-103

    Sinha, Ankur (et al.)

  • Many-objective Optimization Using Evolutionary Algorithms: A Survey

    Pages 105-137

    Bechikh, Slim (et al.)

  • On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking

    Pages 139-157

    Gupta, Abhishek (et al.)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-42978-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-319-42977-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $149.99
price for USA in USD
  • ISBN 978-3-319-82709-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Recent Advances in Evolutionary Multi-objective Optimization
Editors
  • Slim Bechikh
  • Rituparna Datta
  • Abhishek Gupta
Series Title
Adaptation, Learning, and Optimization
Series Volume
20
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-42978-6
DOI
10.1007/978-3-319-42978-6
Hardcover ISBN
978-3-319-42977-9
Softcover ISBN
978-3-319-82709-4
Series ISSN
1867-4534
Edition Number
1
Number of Pages
XII, 179
Number of Illustrations
15 b/w illustrations, 27 illustrations in colour
Topics