Articles liés à Stable Mutations for Evolutionary Algorithms
Synopsis
Chapter 1. Introduction.- Chapter 2. Foundations of evolutionary algorithms.- Chapter 3. Stable distributions.- Chapter 4. Non-isotropic stable mutation.- Chapter 5. Isotropic stable mutation.- Chapter 6. Stable mutation with the discrete spectral measure.- Chapter 7. Isotropic mutation based on an α -stable generator.- Chapter 8. Mutation based on directional distributions.- Chapter 9. Conclusions.- References.- Appendix A Saddle crossing problem.- Appendix B Benchmark functions.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Stable Mutations for Evolutionary Algorithms.
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Cham, Springer., 2019
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xiv, 164 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch. N° de réf. du vendeur 2743MB
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Stable Mutations for Evolutionary Algorithms (Studies in Computational Intelligence, 797)
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Stable Mutations for Evolutionary Algorithms
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Stable Mutations for Evolutionary Algorithms (Studies in Computational Intelligence, 797)
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Stable Mutations for Evolutionary Algorithms (Hardcover)
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Springer Nature Switzerland AG, Cham, 2018
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Hardcover. Etat : new. Hardcover. This book presents a set of theoretical and experimental results that describe the features of the wide family of a-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 a-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 a-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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783030015473
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Stable Mutations for Evolutionary Algorithms
Edité par
Springer, 2018
ISBN 10 : 3030015475
ISBN 13 : 9783030015473
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Stable Mutations for Evolutionary Algorithms (Studies in Computational Intelligence, 797)
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Stable Mutations for Evolutionary Algorithms (Studies in Computational Intelligence, 797)
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Stable Mutations for Evolutionary Algorithms
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Springer International Publishing Okt 2018, 2018
ISBN 10 : 3030015475
ISBN 13 : 9783030015473
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a set of theoretical and experimental results that describe the features of the wide family of -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 -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 -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. 180 pp. Englisch. N° de réf. du vendeur 9783030015473
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Stable Mutations for Evolutionary Algorithms
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