Web搜索中基于免疫克隆算法的信息检索方法

An Information Retrieval Method Based on the Immune Clonal Algorithm in Web Search

随着万维网上信息的爆炸性增长,导致需要大量有效的信息检索方法。本文对传统的向量空间模型进行了优化,并提出一种基于克隆选择算法的Web搜索方法。通过对查询结果的精确率和混合检索率进行比较,表明该算法能有效提高Web搜索引擎的质量和运行效率。

With the exponential growth of information on the World Wide Web, there is a great demand for developing efficient methods for effectively organizing the large amount of retrieved information. In this paper, the traditional vector space model is optimized. A web search method based on the clonal selection algorithm is proposed. Through comparing with the precision and hybrid retrieval rates, the results show this adaptive algorithm enhances the quality and efficiency, and the consequence of the retrieval is satisfactory.