Adaptation, Learning, and Optimization

Adaptive Differential Evolution

A Robust Approach to Multimodal Problem Optimization

Authors: Zhang, Jingqiao, Sanderson, Arthur C.

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  • Comprehensive study of adaptive differential evolution
  • Real-world insights into a variety of large-scale complex industrial applications
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eBook $119.00
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valid through March 31, 2021
  • ISBN 978-3-642-01527-4
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Hardcover $159.99
price for USA in USD
valid through March 31, 2021
  • ISBN 978-3-642-01526-7
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  • Covid-19 shipping restrictions & severe weather in the US may cause delays
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Softcover $159.99
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valid through March 31, 2021
  • ISBN 978-3-642-26021-6
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  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
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About this book

Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions.

Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include:

  • Theoretical analysis of differential evolution and its control parameters
  • Algorithmic design and comparative analysis of parameter adaptive schemes
  • Scalability analysis of adaptive differential evolution
  • Adaptive differential evolution for multi-objective optimization
  • Incorporation of surrogate model for computationally expensive optimization
  • Application to winner determination in combinatorial auctions of E-Commerce
  • Application to flight route planning in Air Traffic Management
  • Application to transition probability matrix optimization in credit-decision making

Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook $119.00
price for USA in USD
valid through March 31, 2021
  • ISBN 978-3-642-01527-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
valid through March 31, 2021
  • ISBN 978-3-642-01526-7
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover $159.99
price for USA in USD
valid through March 31, 2021
  • ISBN 978-3-642-26021-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Adaptive Differential Evolution
Book Subtitle
A Robust Approach to Multimodal Problem Optimization
Authors
Series Title
Adaptation, Learning, and Optimization
Series Volume
1
Copyright
2009
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-01527-4
DOI
10.1007/978-3-642-01527-4
Hardcover ISBN
978-3-642-01526-7
Softcover ISBN
978-3-642-26021-6
Series ISSN
1867-4534
Edition Number
1
Number of Pages
XIII, 164
Topics