A Timed-Based Approach for Genetic Algorithm: Theory and Applications
Summary :
In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.6 pp.1306-1320
- Publication Date
- 2011/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1306
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
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Amir MEHRAFSA, Alireza SOKHANDAN, Ghader KARIMIAN, "A Timed-Based Approach for Genetic Algorithm: Theory and Applications" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 6, pp. 1306-1320, June 2011, doi: 10.1587/transinf.E94.D.1306.
Abstract: In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1306/_p
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@ARTICLE{e94-d_6_1306,
author={Amir MEHRAFSA, Alireza SOKHANDAN, Ghader KARIMIAN, },
journal={IEICE TRANSACTIONS on Information},
title={A Timed-Based Approach for Genetic Algorithm: Theory and Applications},
year={2011},
volume={E94-D},
number={6},
pages={1306-1320},
abstract={In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.},
keywords={},
doi={10.1587/transinf.E94.D.1306},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - A Timed-Based Approach for Genetic Algorithm: Theory and Applications
T2 - IEICE TRANSACTIONS on Information
SP - 1306
EP - 1320
AU - Amir MEHRAFSA
AU - Alireza SOKHANDAN
AU - Ghader KARIMIAN
PY - 2011
DO - 10.1587/transinf.E94.D.1306
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2011
AB - In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.
ER -
English
