Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning

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

3 被引用数 (Scopus)

抄録

Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.

本文言語English
ホスト出版物のタイトルSICE Annual Conference, SICE 2007
ページ1341-1347
ページ数7
DOI
出版ステータスPublished - 2007 12 1
イベントSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
継続期間: 2007 9 172007 9 20

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
国/地域Japan
CityTakamatsu
Period07/9/1707/9/20

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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