• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of artificial life research >GA on IR: Study the Effectiveness of the Developed Fitness Function on IR
【24h】

GA on IR: Study the Effectiveness of the Developed Fitness Function on IR

机译：遗传算法在IR上的应用：研究已开发的健身函数对IR的有效性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Increasing the growth rates of websites' number has led to the challenge of assisting Web customers in finding appropriate details from the Internet using an intelligent search engine. Information retrieval (IR) is an essential and useful strategy for Web users; thus, different strategies and techniques are designed for such purpose. Currently, the focus on the usefulness of Artificial Intelligence (AI) has been improved with IR. One AI area is Evolutionary Computation (EC), which is based on designs of natural selection. A traditional and important strategy in EC is Genetic Algorithm (GA); this paper adopts the GA technique to enhance the retrieval of HTML documents. This improvement is obtained by creating a modern evaluation function and applying a hybrid crossover operator. The proposed evaluation function is based on term proximity, keyword probability within the document, and HTML tag weight query. Experimental results are compared with two well known evaluation function functions applied in IR domain which are Okapi-BM25 and Bayesian interface network model. The results demonstrate a good level of enhancement to the recall and precision. In addition, the documents retrieved by the proposed system were more accurate and relevant to the queries than that retrieved by other models.
机译：网站数量增长率的提高带来了挑战，即使用智能搜索引擎来帮助Web客户从Internet查找适当的详细信息。信息检索（IR）是Web用户必不可少的有用策略。因此，为此目的设计了不同的策略和技术。当前，通过IR改进了对人工智能（AI）有用性的关注。人工智能的一个领域是基于自然选择设计的进化计算（EC）。遗传算法（GA）是EC中传统且重要的策略。本文采用GA技术来增强HTML文档的检索。通过创建现代评估函数并应用混合交叉算子可以获得此改进。提议的评估功能基于术语接近度，文档中的关键字概率以及HTML标签权重查询。将实验结果与两个在红外领域应用的知名评估函数Okapi-BM25和贝叶斯接口网络模型进行了比较。结果表明，召回率和准确性得到了很好的提高。此外，与其他模型所检索的文档相比，所提出的系统所检索的文档更加准确且与查询相关。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号