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  • 标题:Rule-based Classifier via Fitness Scaling Genetic Algorithm
  • 本地全文:下载
  • 作者:Yi Yan ; Zucheng DAI
  • 期刊名称:Advances in Computer Science and its Applications
  • 印刷版ISSN:2166-2924
  • 出版年度:2012
  • 卷号:1
  • 期号:3
  • 页码:201-205
  • 语种:English
  • 出版社:World Science Publisher
  • 摘要:The global optimization is the best choice for parameter extraction of rule-based classifier. Restricted methods have been published, and their limitations are concerned mainly on the slow convergence and being trapped into local minima. To resolve the matter, this paper introduced in the fitness scaling genetic algorithm (FSGA) which conducted the heuristic search as the parameter optimization for rule-based classifier. The FSGA rule-based classifier was compared with GA, SA, and ACA, and the results prove that the proposed FSGA rule-based classifier is the most robust and rapid.
  • 关键词:Pattern classification;Rule-based;Classifier;Fitness Scaling;Genetic Algorithm;Heuristic Search;Ant colony Algorithm;Simulated Annealing
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