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Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05)
Efficiency of Local Genetic Algorithm in Parallel Processing
Dalian, China
December 05-December 08
ISBN: 0-7695-2405-2
ASCII Text
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PENG Gang, Ichiro IIMURA, Takeshi NAKATSURU, Shigeru NAKAYAMA, "Efficiency of Local Genetic Algorithm in Parallel Processing," Parallel and Distributed Computing Applications and Technologies, International Conference on, pp. 620-623, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005.
 
 
BibTex
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@article{ 10.1109/PDCAT.2005.129,
author = {PENG Gang and Ichiro IIMURA and Takeshi NAKATSURU and Shigeru NAKAYAMA},
title = {Efficiency of Local Genetic Algorithm in Parallel Processing},
journal ={Parallel and Distributed Computing Applications and Technologies, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2405-2},
pages = {620-623},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2005.129},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Parallel and Distributed Computing Applications and Technologies, International Conference on
TI - Efficiency of Local Genetic Algorithm in Parallel Processing
SN - 0-7695-2405-2
SP620
EP623
A1 - PENG Gang,
A1 - Ichiro IIMURA,
A1 - Takeshi NAKATSURU,
A1 - Shigeru NAKAYAMA,
PY - 2005
KW - genetic algorithm (GA)
KW - parallel GA
KW - global GA
KW - local GA
KW - object shared space
KW - Traveling Salesman Problem
VL - 0
JA - Parallel and Distributed Computing Applications and Technologies, International Conference on
ER -
 
PENG Gang, Oita National College of Technology Maki, Oita City, Japan
Ichiro IIMURA, Prefectural University of Kumamoto, Tsukide, Kumamoto, Japan
Takeshi NAKATSURU, Prefectural University of Kumamoto, Tsukide, Kumamoto, Japan
Shigeru NAKAYAMA, Kagoshima University, Japan
This paper discusses a parallel genetic algorithm (GA) which focuses on the local operator for Traveling salesman problem (TSP). The local operator is a simple GA named as Local Genetic Algorithm (LGA). The LGA is combined to another GA named as Global Genetic Algorithm (GGA). It increases the computational time running a GA as a local operator in another one. To solve this problem, we build a parallel system based on our previous works for running the LGA to speed up the process. The results show that LGA improve the search quality significantly and it is more efficient running LGA with parallel system than single CPU.
Index Terms:
genetic algorithm (GA), parallel GA, global GA, local GA, object shared space, Traveling Salesman Problem
Citation:
PENG Gang, Ichiro IIMURA, Takeshi NAKATSURU, Shigeru NAKAYAMA, "Efficiency of Local Genetic Algorithm in Parallel Processing," pdcat, pp.620-623, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005
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