About this book
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 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.
Editors and affiliations
- Book Title Massively Parallel Evolutionary Computation on GPGPUs
- Series Title Natural Computing Series
- Series Abbreviated Title Natural Computing Series
- DOI https://doi.org/10.1007/978-3-642-37959-8
- Copyright Information Springer-Verlag Berlin Heidelberg 2013
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-642-37958-1
- Softcover ISBN 978-3-662-51345-3
- eBook ISBN 978-3-642-37959-8
- Series ISSN 1619-7127
- Edition Number 1
- Number of Pages XII, 453
- Number of Illustrations 104 b/w illustrations, 95 illustrations in colour
Theory of Computation
Computer Systems Organization and Communication Networks
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