Iterative Parallel Genetic Algorithms Based on Biased Initial Population
Summary :
This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.4 pp.923-929
- Publication Date
- 2005/04/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.4.923
- Type of Manuscript
- Special Section PAPER (Special Section on Selected Papers from the 17th Workshop on Circuits and Systems in Karuizawa)
- Category
Authors
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Morikazu NAKAMURA, Naruhiko YAMASHIRO, Yiyuan GONG, Takashi MATSUMURA, Kenji ONAGA, "Iterative Parallel Genetic Algorithms Based on Biased Initial Population" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 4, pp. 923-929, April 2005, doi: 10.1093/ietfec/e88-a.4.923.
Abstract: This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.4.923/_p
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@ARTICLE{e88-a_4_923,
author={Morikazu NAKAMURA, Naruhiko YAMASHIRO, Yiyuan GONG, Takashi MATSUMURA, Kenji ONAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Iterative Parallel Genetic Algorithms Based on Biased Initial Population},
year={2005},
volume={E88-A},
number={4},
pages={923-929},
abstract={This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.},
keywords={},
doi={10.1093/ietfec/e88-a.4.923},
ISSN={},
month={April},}
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TY - JOUR
TI - Iterative Parallel Genetic Algorithms Based on Biased Initial Population
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 923
EP - 929
AU - Morikazu NAKAMURA
AU - Naruhiko YAMASHIRO
AU - Yiyuan GONG
AU - Takashi MATSUMURA
AU - Kenji ONAGA
PY - 2005
DO - 10.1093/ietfec/e88-a.4.923
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2005
AB - This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.
ER -
English
