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Massively Parallel Evolutionary Computation on GPGPUs

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Table of Contents

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    Book Overview
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    Chapter 1 Why GPGPUs for Evolutionary Computation?
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    Chapter 2 Understanding NVIDIA GPGPU Hardware
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    Chapter 3 Automatic Parallelization of EC on GPGPUs and Clusters of GPGPU Machines with EASEA and EASEA-CLOUD
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    Chapter 4 Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip
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    Chapter 5 arGA: Adaptive Resolution Micro-genetic Algorithm with Tabu Search to Solve MINLP Problems Using GPU
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    Chapter 6 An Analytical Study of Parallel GA with Independent Runs on GPUs
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    Chapter 7 Many-Threaded Differential Evolution on the GPU
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    Chapter 8 Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units
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    Chapter 9 ACO with Tabu Search on GPUs for Fast Solution of the QAP
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    Chapter 10 New Ideas in Parallel Metaheuristics on GPU: Systolic Genetic Search
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    Chapter 11 Genetic Programming on GPGPU Cards Using EASEA
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    Chapter 12 Cartesian Genetic Programming on the GPU
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    Chapter 13 Implementation Techniques for Massively Parallel Multi-objective Optimization
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    Chapter 14 Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units
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    Chapter 15 Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units
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    Chapter 16 GPU-Accelerated High-Accuracy Molecular Docking Using Guided Differential Evolution
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    Chapter 17 Using Large-Scale Parallel Systems for Complex Crystallographic Problems in Materials Science
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    Chapter 18 Artificial Chemistries on GPU
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    Chapter 19 Acceleration of Genetic Algorithms for Sudoku Solution on Many-Core Processors
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4 tweeters
1 Wikipedia page


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Massively Parallel Evolutionary Computation on GPGPUs
Published by
Springer-Verlag Berlin Heidelberg, January 2013
DOI 10.1007/978-3-642-37959-8
978-3-64-237958-1, 978-3-64-237959-8, 978-3-66-251345-3

Shigeyoshi Tsutsui, Pierre Collet

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 1%
Student > Postgraduate 1 1%
Unknown 74 97%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 1%
Computer Science 1 1%
Unknown 74 97%