Parallel Genetic Algorithm for Constrained Clustering
Summary :
In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.2 pp.416-422
- Publication Date
- 1997/02/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Modeling and Simulation
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Myung-Mook HAN, Shoji TATSUMI, Yasuhiko KITAMURA, Takaaki OKUMOTO, "Parallel Genetic Algorithm for Constrained Clustering" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 2, pp. 416-422, February 1997, doi: .
Abstract: In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_2_416/_p
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@ARTICLE{e80-a_2_416,
author={Myung-Mook HAN, Shoji TATSUMI, Yasuhiko KITAMURA, Takaaki OKUMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Parallel Genetic Algorithm for Constrained Clustering},
year={1997},
volume={E80-A},
number={2},
pages={416-422},
abstract={In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Parallel Genetic Algorithm for Constrained Clustering
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 416
EP - 422
AU - Myung-Mook HAN
AU - Shoji TATSUMI
AU - Yasuhiko KITAMURA
AU - Takaaki OKUMOTO
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 1997
AB - In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.
ER -
English
