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The engineering and business problems the world faces today have
become more impenetrable and unstructured, making the design of a
satisfactory problem-specific algorithm nontrivial. Modeling,
Analysis, and Applications in Metaheuristic Computing: Advancements
and Trends is a collection of the latest developments, models, and
applications within the transdisciplinary fields related to
metaheuristic computing. Providing researchers, practitioners, and
academicians with insight into a wide range of topics such as
genetic algorithms, differential evolution, and ant colony
optimization, this book compiles the latest findings, analysis,
improvements, and applications of technologies within metaheuristic
computing.
Developments in metaheuristics continue to advance computation
beyond its traditional methods. With groundwork built on
multidisciplinary research findings; metaheuristics, algorithms,
and optimization approaches uses memory manipulations in order to
take full advantage of strategic level problem solving.Trends in
Developing Metaheuristics, Algorithms, and Optimization Approaches
provides insight on the latest advances and analysis of
technologies in metaheuristics computing. Offering widespread
coverage on topics such as genetic algorithms, differential
evolution, and ant colony optimization, this book aims to be a
forum researchers, practitioners, and students who wish to learn
and apply metaheuristic computing.
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