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An adaptive resolution hybrid binary-real coded genetic algorithm

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/46831

Title: An adaptive resolution hybrid binary-real coded genetic algorithm
Authors: Abdul-Rahman, Omar Arif Browse this author
Munetomo, Masaharu Browse this author →KAKEN DB
Akama, Kiyoshi Browse this author
Keywords: Binary-coded GA
Real-coded GA
Hybrid scheme
Issue Date: 2011
Publisher: Springer
Journal Title: Artificial Life and Robotics
Volume: 16
Issue: 1
Start Page: 121
End Page: 124
Publisher DOI: 10.1007/s10015-011-0906-z
Abstract: 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
Type: article (author version)
URI: http://hdl.handle.net/2115/46831
Appears in Collections:情報基盤センター (Information Initiative Center) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 棟朝 雅晴

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