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基于遗传算法的全局优化检索策略研究
Strategy research of global optimization based on genetic algorithm
【摘要】 案例检索是基于案例推理(CBR)系统中的关键技术,也是实现智能挖掘系统的关键环节。为了能够进一步提高案例检索效率与准确性,传统研究多是从案例属性和案例库的约减两方面入手,但是没有考虑案例属性权重。提出了一种基于遗传算法的全局优化案例检索模型,该模型利用遗传算法在搜索优化上的优势,对案例库、属性权重、K-NN中的K值进行全局同步优化。最后,通过实验验证了该模型在检索效率与准确性上优于传统模型。
【Abstract】 Case retrieval is the key technique in CBR,and it is also the most important step in realizing artificial mining system.To improve the efficiency and accuracy of case retrieval,most traditional researches focus on attribute reduction and filter casebase,but they do not take attribute weights and K of K-NN into account.This paper proposed a new global optimization research technique based on genetic algorithm.This technique,taking advantage of genetic algorithm,can optimize case-base,attribute weights,and K of K-NN algorithm synchronously.At last,experiments show that searching efficiency and accuracy under this technique are priorer than traditional technique in efficiency and accuracy.
【Key words】 K-NN; genetic algorithm; case retrieval; CBR; global optimization;
- 【文献出处】 计算机应用研究 ,Application Research of Computers , 编辑部邮箱 ,2009年05期
- 【分类号】TP391.3
- 【被引频次】8
- 【下载频次】306