Stable Mutations for Evolutionary Algorithms
Language: English
Published by Springer International Publishing, 2018
Series: Book 305 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Condition: New
£ 80.30
Quantity: Over 20 available
Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Collects recent research on stable mutations for evolutionary algorithmsPresents theoretical analysis and simulation experiments, selected and constructed to expose the most important features of the examined mutation techniques based on ?-stable .
Seller Inventory # 244074662
- Title
- Stable Mutations for Evolutionary Algorithms
- Author
- Andrzej Obuchowicz
- Publisher
- Springer International Publishing
- Publication year
- 2018
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 3030015475
- ISBN 13
- 9783030015473
- Series
- Book 305 of 538: Studies in Computational Intelligence
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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."Synopsis" may belong to another edition of this title.
From the Back Cover
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."About the title" may belong to another edition of this title.
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