000 05483nam a22005775i 4500
001 978-3-642-37959-8
003 DE-He213
005 20200421111703.0
007 cr nn 008mamaa
008 131205s2013 gw | s |||| 0|eng d
020 _a9783642379598
_9978-3-642-37959-8
024 7 _a10.1007/978-3-642-37959-8
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aMassively Parallel Evolutionary Computation on GPGPUs
_h[electronic resource] /
_cedited by Shigeyoshi Tsutsui, Pierre Collet.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXII, 453 p. 199 illus., 95 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x1619-7127
505 0 _aChap. 1 Why GPGPUs for Evolutionary Computation? -- Chap. 2 Understanding NVIDIA GPGPU Hardware -- Chap. 3 Automatic Parallelization of EC on GPGPUs and Clusters of GPGPU Machines with EASEA and EASEA-CLOUD -- Chap. 4 Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip -- Chap. 5 arGA: Adaptive Resolution Micro-genetic Algorithm with Tabu Search to Solve MINLP Problems Using GPU -- Chap. 6 An Analytical Study of GPU Computation by Parallel GA with Independent Runs -- Chap. 7 Many-Threaded Differential Evolution on the GPU -- Chap. 8 Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units -- Chap. 9 ACO with Tabu Search on GPUs for Fast Solution of the QAP -- Chap. 10 New Ideas in Parallel Metaheuristics on GPU: Systolic Genetic Search -- Chap. 11 Genetic Programming on GPGPU Cards Using EASEA -- Chap. 12 Cartesian Genetic Programming on the GPU -- Chap. 13 Implementation Techniques for Massively Parallel Multi-objective Optimization -- Chap. 14 Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units -- Chap. 15 Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units -- Chap. 16 GPU-Accelerated High-Accuracy Molecular Docking Using Guided Differential Evolution -- Chap. 17 Using Large-Scale Parallel Systems for Complex Crystallographic Problems in Materials Science -- Chap. 18 Artificial Chemistries on GPU -- Chap. 19 Acceleration of Genetic Algorithms for Sudoku Solution on Many-Core Processors.
520 _aEvolutionary 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 ten 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 six 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.
650 0 _aComputer science.
650 0 _aComputer organization.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 0 _aElectrical engineering.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aComputer Systems Organization and Communication Networks.
650 2 4 _aElectrical Engineering.
700 1 _aTsutsui, Shigeyoshi.
_eeditor.
700 1 _aCollet, Pierre.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642379581
830 0 _aNatural Computing Series,
_x1619-7127
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-37959-8
912 _aZDB-2-SCS
942 _cEBK
999 _c55103
_d55103