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  • 标题:Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms
  • 本地全文:下载
  • 作者:Youhei Akimoto ; Yuichi Nagata ; Jun Sakuma
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2010
  • 卷号:25
  • 期号:2
  • 页码:281-289
  • DOI:10.1527/tjsai.25.281
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model.
  • 关键词:function optimization ; minimal generation gap ; just generation gap
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