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Massively Parallel Evolutionary Computation on GPGPUs

Overview of attention for book
Massively Parallel Evolutionary Computation on GPGPUs
Springer-Verlag Berlin Heidelberg

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
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

twitter
4 X users
wikipedia
3 Wikipedia pages

Readers on

mendeley
76 Mendeley
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Title
Massively Parallel Evolutionary Computation on GPGPUs
Published by
Natural Computing Series, January 2013
DOI 10.1007/978-3-642-37959-8
ISBNs
978-3-64-237958-1, 978-3-64-237959-8, 978-3-66-251345-3
Editors

Shigeyoshi Tsutsui, Pierre Collet

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2024.
All research outputs
#6,677,913
of 26,105,177 outputs
Outputs from Natural Computing Series
#8
of 22 outputs
Outputs of similar age
#64,259
of 293,192 outputs
Outputs of similar age from Natural Computing Series
#1
of 2 outputs
Altmetric has tracked 26,105,177 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 22 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one scored the same or higher as 14 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 293,192 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them