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  • 标题:Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems
  • 本地全文:下载
  • 作者:Lim, Siew Mooi ; Sulaiman, Md. Nasir ; Mustapha, Norwati
  • 期刊名称:Journal of Computer Science
  • 印刷版ISSN:1549-3636
  • 出版年度:2015
  • 卷号:11
  • 期号:11
  • 页码:1025-1031
  • DOI:10.3844/jcssp.2015.1025.1031
  • 出版社:Science Publications
  • 摘要:This study operates within experimental design with two main tools of Taguchi method namely orthogonal array and signal to noise ratio to discover the optimal parameter settings for newly proposed generational genetic algorithms; they are Laplace Crossover-Scale Truncated Pareto Mutation (LX-STPM) and Rayleigh Crossover-Scale Truncated Pareto Mutation (RX-STPM). It concluded that GA parameter settings are algorithms and problems dependent.
  • 关键词:Genetic Algorithms; Parameter Settings; Taguchi Method
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