Performance tuning of genetic algorithms with reserve selection

Yang Chen*, Jinglu Hu, Kotaro Hirasawa, Songnian Yu

*この研究の対応する著者

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper provides a deep insight into the performance of genetic algorithms with reserve selection (GARS), and investigates how parameters can be regulated to solve optimization problems more efficiently. First of all, we briefly present GARS, an improved genetic algorithm with a reserve selection mechanism which helps to avoid premature convergence. The comparable results to state-of-the-art techniques such as fitness scaling and sharing demonstrate both the effectiveness and the robustness of GARS in global optimization. Next, two strategies named Static RS and Dynamic RS are proposed for tuning the parameter reserve size to optimize the performance of GARS. Empirical studies conducted in several cases indicate that the optimal reserve size is problem dependent.

本文言語English
ホスト出版物のタイトル2007 IEEE Congress on Evolutionary Computation, CEC 2007
ページ2202-2209
ページ数8
DOI
出版ステータスPublished - 2007 12 1
イベント2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
継続期間: 2007 9 252007 9 28

出版物シリーズ

名前2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
国/地域Singapore
Period07/9/2507/9/28

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

フィンガープリント

「Performance tuning of genetic algorithms with reserve selection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル