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An adaptive resolution hybrid binary-real coded genetic algorithm

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この文献へのリンクには次のURLを使用してください:http://hdl.handle.net/2115/46831

タイトル: An adaptive resolution hybrid binary-real coded genetic algorithm
著者: Abdul-Rahman, Omar Arif 著作を一覧する
Munetomo, Masaharu 著作を一覧する
Akama, Kiyoshi 著作を一覧する
キーワード: Binary-coded GA
Real-coded GA
Hybrid scheme
発行日: 2011年
出版者: Springer
誌名: Artificial Life and Robotics
巻: 16
号: 1
開始ページ: 121
終了ページ: 124
出版社 DOI: 10.1007/s10015-011-0906-z
抄録: In genetic algorithms (GAs), is it better to use binary encoding schemes or floating point encoding schemes? In this article, we try to tackle this controversial question by proposing a novel algorithm that divides the computational power between two cooperative versions of GAs. These are a binary-coded GA (bGA) and a real-coded GA (rGA). The evolutionary search is primarily led by the bGA, which identifi es promising regions in the search space, while the rGA increases the quality of the solutions obtained by conducting an exhaustive search throughout these regions. The resolution factor (R), which has a value that is increasingly adapted during the search, controls the interactions between the two versions. We conducted comparison experiments employing a typical benchmark function to prove the feasibility of the algorithm under the critical scenarios of increasing problem dimensions and decreasing precision power.
Rights: The original publication is available at www.springerlink.com
資料タイプ: article (author version)
URI: http://hdl.handle.net/2115/46831
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 棟朝 雅晴

 

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