GARS: An improved genetic algorithm with reserve selection for global optimization

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

12 被引用数 (Scopus)

抄録

This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted in less-fit individuals during the course of evolution. Thus, we propose a new improver - GAs with Reserve Selection (GARS), where a reserved area is set up to save potential building blocks and a selection mechanism based on individual uniqueness is employed to activate the potentials. Finally, case studies are done in a few standard problems well known in the literature, where the experimental results demonstrate the effectiveness and robustness of GARS in suppressing premature convergence, and also an enhancement is found in global optimization capacity.

本文言語English
ホスト出版物のタイトルProceedings of GECCO 2007
ホスト出版物のサブタイトルGenetic and Evolutionary Computation Conference
ページ1173-1178
ページ数6
DOI
出版ステータスPublished - 2007 8 27
イベント9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
継続期間: 2007 7 72007 7 11

出版物シリーズ

名前Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Conference

Conference9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
国/地域United Kingdom
CityLondon
Period07/7/707/7/11

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

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