Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

A Parallel Genetic Algorithm Based on MPI for N-Queen

Authors
Kaiyu Wang, Zhenzhou Ji, Yihao Zhou
Corresponding Author
Kaiyu Wang
Available Online June 2017.
DOI
10.2991/caai-17.2017.85How to use a DOI?
Keywords
genetic algorithm; MPI; parallel technique; numerous data
Abstract

Genetic Algorithm which has certain ability to learn is an ideal tool to solve the complicated problems. Genetic Algorithm follow the Darwinian evolution, the model N-Queens is classic NP problems the problem scale is exponential growth with the growth of queens. So it costs a mass of time to solve it in numerous data. Therefore the genetic algorithm for parallel processing to solve large-scale NP has great significance. In the thesis we discuss the genetic algorithm and parallel technique, we have improved the technique specialized in the genetic algorithm and proved that the genetic algorithm can be speed up through lots of experiences and make optimization for the possible of early convergence and present a solution.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
10.2991/caai-17.2017.85How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Kaiyu Wang
AU  - Zhenzhou Ji
AU  - Yihao Zhou
PY  - 2017/06
DA  - 2017/06
TI  - A Parallel Genetic Algorithm Based on MPI for N-Queen
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 375
EP  - 378
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.85
DO  - 10.2991/caai-17.2017.85
ID  - Wang2017/06
ER  -