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Books > Computing & IT > Computer communications & networking
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Massively Parallel Evolutionary Computation on GPGPUs (Paperback, Softcover reprint of the original 1st ed. 2013)
Loot Price: R2,813
Discovery Miles 28 130
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Massively Parallel Evolutionary Computation on GPGPUs (Paperback, Softcover reprint of the original 1st ed. 2013)
Series: Natural Computing Series
Expected to ship within 10 - 15 working days
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Evolutionary algorithms (EAs) are metaheuristics that learn from
natural collective behavior and are applied to solve optimization
problems in domains such as scheduling, engineering,
bioinformatics, and finance. Such applications demand acceptable
solutions with high-speed execution using finite computational
resources. Therefore, there have been many attempts to develop
platforms for running parallel EAs using multicore machines,
massively parallel cluster machines, or grid computing
environments. Recent advances in general-purpose computing on
graphics processing units (GPGPU) have opened up this possibility
for parallel EAs, and this is the first book dedicated to this
exciting development. The three chapters of Part I are tutorials,
representing a comprehensive introduction to the approach,
explaining the characteristics of the hardware used, and presenting
a representative project to develop a platform for automatic
parallelization of evolutionary computing (EC) on GPGPUs. The 10
chapters in Part II focus on how to consider key EC approaches in
the light of this advanced computational technique, in particular
addressing generic local search, tabu search, genetic algorithms,
differential evolution, swarm optimization, ant colony
optimization, systolic genetic search, genetic programming, and
multiobjective optimization. The 6 chapters in Part III present
successful results from real-world problems in data mining,
bioinformatics, drug discovery, crystallography, artificial
chemistries, and sudoku. Although the parallelism of EAs is suited
to the single-instruction multiple-data (SIMD)-based GPU, there are
many issues to be resolved in design and implementation, and a key
feature of the contributions is the practical engineering advice
offered. This book will be of value to researchers, practitioners,
and graduate students in the areas of evolutionary computation and
scientific computing.
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