Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

Efficient Query for Historical Data in Evolutionary Algorithm

Authors
Jie TIAN, Pan YAN, Huiwen HUANG
Corresponding Author
Jie TIAN
Available Online July 2017.
DOI
https://doi.org/10.2991/eia-17.2017.4How to use a DOI?
Keywords
fitness inheritance; fitness estimation; computationally expensive optimization
Abstract

For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
Series
Advances in Intelligent Systems Research
Publication Date
July 2017
ISBN
10.2991/eia-17.2017.4
ISSN
1951-6851
DOI
https://doi.org/10.2991/eia-17.2017.4How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jie TIAN
AU  - Pan YAN
AU  - Huiwen HUANG
PY  - 2017/07
DA  - 2017/07
TI  - Efficient Query for Historical Data in Evolutionary Algorithm
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 13
EP  - 19
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.4
DO  - https://doi.org/10.2991/eia-17.2017.4
ID  - TIAN2017/07
ER  -