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Studies in Computational Intelligence

Stable Mutations for Evolutionary Algorithms

Authors: Obuchowicz, Andrzej

  • Collects recent research on stable mutations for evolutionary algorithms
  • Presents theoretical analysis and simulation experiments, selected and constructed to expose the most important features of the examined mutation techniques based on α-stable distributions
  • Demonstrates that the correct choice of probabilistic model of the mutation operator for an optimization problem is a crucial factor
  • Allows readers to develop a deeper understanding of evolutionary processes with stable mutations and encourages them to apply these techniques to real-world engineering problems
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eBook 101,14 €
price for Spain (gross)
  • ISBN 978-3-030-01548-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-030-01547-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book presents a set of theoretical and experimental results that describe the features of the wide family of α-stable distributions (the normal distribution also belongs to this class) and their various applications in the mutation operator of evolutionary algorithms based on real-number representation of the individuals, and, above all, equip these algorithms with features that enrich their effectiveness in solving multi-modal, multi-dimensional global optimization problems. The overall conclusion of the research presented is that the appropriate choice of probabilistic model of the mutation operator for an optimization problem is crucial.

Mutation is one of the most important operations in stochastic global optimization algorithms in the n-dimensional real space. It determines the method of search space exploration and exploitation. Most applications of these algorithms employ the normal mutation as a mutation operator. This choice is justified by the central limit theorem but is associated with a set of important limitations. Application of α-stable distributions allows more flexible evolutionary models to be obtained than those with the normal distribution. The book presents theoretical analysis and simulation experiments, which were selected and constructed to expose the most important features of the examined mutation techniques based on α-stable distributions. It allows readers to develop a deeper understanding of evolutionary processes with stable mutations and encourages them to apply these techniques to real-world engineering problems.

Table of contents (9 chapters)

Buy this book

eBook 101,14 €
price for Spain (gross)
  • ISBN 978-3-030-01548-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-030-01547-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Stable Mutations for Evolutionary Algorithms
Authors
Series Title
Studies in Computational Intelligence
Series Volume
797
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-01548-0
DOI
10.1007/978-3-030-01548-0
Hardcover ISBN
978-3-030-01547-3
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XIV, 164
Number of Illustrations
58 b/w illustrations, 11 illustrations in colour
Topics