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Evolutionary algorithms (EAs) are metaheuristics that study from pure collective conduct and are utilized to unravel optimization issues in domains resembling scheduling, engineering, bioinformatics, and finance. Such purposes demand acceptable options with high-speed execution utilizing finite computational assets. Subsequently, there have been many makes an attempt to develop platforms for operating parallel EAs utilizing multicore machines, massively parallel cluster machines, or grid computing environments. Current advances normally-objective computing on graphics processing models (GPGPU) have opened up this risk for parallel EAs, and that is the primary book devoted to this thrilling improvement.
The three chapters of Half I are tutorials, representing a complete introduction to the strategy, explaining the traits of the hardware used, and presenting a consultant challenge to develop a platform for automated parallelization of evolutionary computing (EC) on GPGPUs. The ten chapters in Half II focus on how one can think about key EC approaches within the light of this superior computational method, particularly 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 Half III current profitable outcomes from actual-world issues in data mining, bioinformatics, drug discovery, crystallography, synthetic chemistries, and sudoku.
Though the parallelism of EAs is suited to the only-instruction a number of-data (SIMD)-based mostly GPU, there are various points to be resolved in design and implementation, and a key function of the contributions is the sensible engineering recommendation provided. This book can be of worth to researchers, practitioners, and graduate college students within the areas of evolutionary computation and scientific computing.
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