IEICE TRANSACTIONS on Information
Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms
Summary :
In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.10 pp.1789-1792
- Publication Date
- 2021/10/01
- Publicized
- 2021/07/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDL8033
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Kaiyu WANG
University of Toyama
Sichen TAO
University of Toyama
Rong-Long WANG
University of Fukui
Yuki TODO
Kanazawa University
Shangce GAO
University of Toyama
Keyword
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Kaiyu WANG, Sichen TAO, Rong-Long WANG, Yuki TODO, Shangce GAO, "Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1789-1792, October 2021, doi: 10.1587/transinf.2021EDL8033.
Abstract: In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8033/_p
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@ARTICLE{e104-d_10_1789,
author={Kaiyu WANG, Sichen TAO, Rong-Long WANG, Yuki TODO, Shangce GAO, },
journal={IEICE TRANSACTIONS on Information},
title={Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms},
year={2021},
volume={E104-D},
number={10},
pages={1789-1792},
abstract={In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.},
keywords={},
doi={10.1587/transinf.2021EDL8033},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 1789
EP - 1792
AU - Kaiyu WANG
AU - Sichen TAO
AU - Rong-Long WANG
AU - Yuki TODO
AU - Shangce GAO
PY - 2021
DO - 10.1587/transinf.2021EDL8033
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
ER -
English
