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Books > Computing & IT > Applications of computing > Artificial intelligence
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Stable Mutations for Evolutionary Algorithms (Hardcover, 1st ed. 2019)
Loot Price: R2,653
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Stable Mutations for Evolutionary Algorithms (Hardcover, 1st ed. 2019)
Series: Studies in Computational Intelligence, 797
Expected to ship within 18 - 22 working days
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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.
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