Parallel Architectures and Bioinspired Algorithms
Published on 28. April 2012
Book
Hardback
VI, 290 pages
978-3-642-28788-6 (ISBN)
Description
This monograph presents examples of best practices when combining bioinspired algorithms with parallel architectures. The book includes recent work by leading researchers in the field and offers a map with the main paths already explored and new ways towards the future.
Parallel Architectures and Bioinspired Algorithms will be of value to both specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to understand the present and the future of Parallel Architectures and Bioinspired Algorithms.
More details
Series
Edition
2012 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VI, 290 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 20 mm
Weight
611 gr
ISBN-13
978-3-642-28788-6 (9783642287886)
DOI
10.1007/978-3-642-28789-3
Schweitzer Classification
Other editions
Additional editions
Francisco Fernández de Vega | José Ignacio Hidalgo Pérez | Juan Lanchares
Parallel Architectures and Bioinspired Algorithms
Book
05/2014
Springer
€160.49
Shipment within 7-9 days
Francisco Fernández de Vega | José Ignacio Hidalgo Pérez | Juan Lanchares
Parallel Architectures and Bioinspired Algorithms
E-Book
04/2012
Springer
€149.79
Available for download
Content
Creating and Debugging Performance CUDA C.- Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs.- Characterizing Fault-tolerance in Genetic Algorithms and programming.- Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems.- An Empirical Study of Parallel and Distributed Particle Swarm Optimization.- The generalized Island Model.- Genetic Programming for the Evolution of Associative Memories.- Parallel Architectures for Improving the Performance of a GA based trading System.- A Knowledge-Based Operator for a Genetic Algorithm which Optimizes the Distribution of Sparse Matrix Data.- Evolutive approaches for Variable Selection using a Non-parametric Noise Estimator.- A chemical evolutionary mechanism for instantiating service-based applications.