IEICE TRANSACTIONS on Fundamentals
An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size
Summary :
This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.2 pp.532-541
- Publication Date
- 2010/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.532
- Type of Manuscript
- PAPER
- Category
- Neural Networks and Bioengineering
Authors
Keyword
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Shangce GAO, Rong-Long WANG, Masahiro ISHII, Zheng TANG, "An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 2, pp. 532-541, February 2010, doi: 10.1587/transfun.E93.A.532.
Abstract: This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.532/_p
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@ARTICLE{e93-a_2_532,
author={Shangce GAO, Rong-Long WANG, Masahiro ISHII, Zheng TANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size},
year={2010},
volume={E93-A},
number={2},
pages={532-541},
abstract={This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.},
keywords={},
doi={10.1587/transfun.E93.A.532},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 532
EP - 541
AU - Shangce GAO
AU - Rong-Long WANG
AU - Masahiro ISHII
AU - Zheng TANG
PY - 2010
DO - 10.1587/transfun.E93.A.532
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2010
AB - This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.
ER -
English
