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The Implementation and Comparison of Two Kinds of Parallel Genetic Algorithm Using Matlab

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4 Author(s)
Li Nan ; High Performance Comput. Center, Commun. Univ. of China, Beijing, China ; Gao Pengdong ; Lu Yongquan ; Yu Wenhua

Two kinds of parallel genetic algorithm (PGA) are implemented in this paper based on the MATLAB® Parallel Computing Toolbox™ and Distributed Computing Server™ software. Parallel for-loops, SPMD (Single Program Multiple Data) block and co-distributed arrays, three basic parallel programming modes in MATLAB are employed to accomplish the global and coarse-grained PGAs. To validate and compare our implementation, both PGAs are applied to run the problem of range image registration. A set of experiments have illustrated that it is convenient and effective to use MATLAB to parallelize the existing algorithms. At the same time, a higher speed-up and performance enhancement can be obtained obviously.

Published in:

Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on

Date of Conference:

10-12 Aug. 2010