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Stable Mutations for Evolutionary Algorithms
Af: Andrzej Obuchowicz Engelsk Paperback
SPAR
kr 253
Stable Mutations for Evolutionary Algorithms
Af: Andrzej Obuchowicz Engelsk Paperback

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.
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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.
Produktdetaljer
Sprog: Engelsk
Sider: 164
ISBN-13: 9783030131845
Indbinding: Paperback
Udgave:
ISBN-10: 303013184X
Udg. Dato: 10 dec 2019
Længde: 0mm
Bredde: 235mm
Højde: 155mm
Oplagsdato: 10 dec 2019
Forfatter(e): Andrzej Obuchowicz
Forfatter(e) Andrzej Obuchowicz


Kategori Algoritmer og datastrukturer


Sprog Engelsk


Indbinding Paperback


Sider 164


Udgave


Længde 0mm


Bredde 235mm


Højde 155mm


Udg. Dato 10 dec 2019


Oplagsdato 10 dec 2019

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