Massively Parallel Evolutionary Computation on GPGPUs

Massively Parallel Evolutionary Computation on GPGPUs

eBook - 2013
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Evolutionary algorithms 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. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up the use of parallel EAs. Topics include: 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; key EC approaches, in particular: generic local search, tabu search, genetic algorithms, differential evolution, swarm and ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization; and successful results from real-world problems. --
Publisher: Heidelberg : Springer, [2013]
Copyright Date: ©2013
ISBN: 9783642379598
Characteristics: 1 online resource : illustrations


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