Fuzzy Adaptive Search Method for Parallel Genetic Algorithm with Island Combination Process

Material type: 文書・図像類

Author: MAEDA, Yoichiroほか

Publisher: Elsevier Ltd

Publication date: 2006-01

Material Format: Digital

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Note (General)：: Genetic algorithms (GAs) pose several problems. Probably, the most important one is that the search ability of ordinary GAs is not always optimal in t...

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Material Type: 文書・図像類
Title: Fuzzy Adaptive Search Method for Parallel Genetic Algorithm with Island Combination Process
Author/Editor: MAEDA, Yoichiro
ISHITA, Masahide
LI, Qiang
Author Heading: MAEDA, Yoichiro
ISHITA, Masahide
LI, Qiang
Publication, Distribution, etc.: Elsevier Ltd
Publication Date: 2006-01
Publication Date (W3CDTF): 2006-01
Periodical title: International Journal of Approximate Reasoning
No. or year of volume/issue: 41 1
Volume: 41
Issue: 1
Pages: 59-73
ISSN (Periodical Title): ISSN : 0888-613X
Text Language Code: eng
Subject Heading: Parallel Genetic Algorithm
Fuzzy Reasoning
Adaptive Search
Migration Rate
Target Audience: 一般
Note (General): Genetic algorithms (GAs) pose several problems. Probably, the most important one is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. To solve this problem, we proposed the fuzzy adaptive search method for genetic algorithms (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. In this paper, a fuzzy adaptive search method for parallel genetic algorithms (FASPGA) is proposed, in which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined with the high-quality solution finding capacity of parallel genetic algorithms. The proposed method offers improved search performance, and produces high-quality solutions. Moreover, we also propose FASPGA with an operation of combining dynamically sub-populations (C-FASPGA) which combines two elite islands in the final stage of the evolution to find a better solution as early as possible. Simulations are performed to confirm the efficiency of the proposed method, which is shown to be superior to both ordinary and parallel genetic algorithms.
Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=22580&item_no=1&attribute_id=22&file_no=1
https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=22580&item_no=1&attribute_id=22&file_no=1
Access Restrictions: 限定公開
Related Material (DOI): 10.1016/j.ijar.2005.06.007
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ