Solving the Graph Planarization Problem Using an Improved Genetic Algorithm
Summary :
An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.5 pp.1507-1512
- Publication Date
- 2006/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.5.1507
- Type of Manuscript
- LETTER
- Category
- Numerical Analysis and Optimization
Authors
Keyword
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Rong-Long WANG, Kozo OKAZAKI, "Solving the Graph Planarization Problem Using an Improved Genetic Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 5, pp. 1507-1512, May 2006, doi: 10.1093/ietfec/e89-a.5.1507.
Abstract: An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.5.1507/_p
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@ARTICLE{e89-a_5_1507,
author={Rong-Long WANG, Kozo OKAZAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Solving the Graph Planarization Problem Using an Improved Genetic Algorithm},
year={2006},
volume={E89-A},
number={5},
pages={1507-1512},
abstract={An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.},
keywords={},
doi={10.1093/ietfec/e89-a.5.1507},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Solving the Graph Planarization Problem Using an Improved Genetic Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1507
EP - 1512
AU - Rong-Long WANG
AU - Kozo OKAZAKI
PY - 2006
DO - 10.1093/ietfec/e89-a.5.1507
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2006
AB - An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.
ER -
English
