Massively Parallel Evolutionary Computation on GPGPUs

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 co...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Tsutsui, Shigeyoshi (Editor ), Collet, Pierre (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Natural Computing Series,
Temas:
Artificial intelligence.
Computational intelligence.
Computer science.
Computer engineering.
Computer networks .
Electrical engineering.
Artificial Intelligence.
Computational Intelligence.
Theory of Computation.
Computer Engineering and Networks.
Electrical and Electronic Engineering.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Chap. 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.

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