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- MAO Jiangming
- Department of Electrical and Electronic Systems Engineering, Kyushu University
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- HIRASAWA Kotaro
- Department of Electrical and Electronic Systems Engineering, Kyushu University
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- HU Jinglu
- Department of Electrical and Electronic Systems Engineering, Kyushu University
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- MURATA Junichi
- Department of Electrical and Electronic Systems Engineering, Kyushu University
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抄録
Evolutionary Algorithms are often well-suited for optimization problems. Since mid 1980's, the interest in multi-objective problems has been expanding rapidly. Various evolutionary algorithms for multiobjective problems have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we propose a new genetic symbiosis algorithm (GSA) for multiobjective optimization problems (MOP) based on the symbiotic concept found widely in ecosystems. In the proposed GSA for MOP, a set of symbiotic parameters are introduced to modify the fitness of individuals used for reproduction so as to obtain a variety of Pareto solutions corresponding to user's demands. The symbiotic parameters are trained by minimizing a user defined criterion function. Several numerical simulations are carried out to demonstrate the effectiveness of the proposed GSA.
収録刊行物
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- 計測自動制御学会論文集
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計測自動制御学会論文集 37 (9), 893-901, 2001
公益社団法人 計測自動制御学会
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詳細情報 詳細情報について
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- CRID
- 1390282679476506240
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- NII論文ID
- 130003971090
- 10006846616
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- NII書誌ID
- AN00072392
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- ISSN
- 18838189
- 04534654
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- NDL書誌ID
- 5911033
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- 使用不可