Result merging in meta-search engine using genetic algorithm

In the recent years, the internet has become one of the most important resource of information. Whenever a user poses a query to the Meta-search engine then a large number of documents are retrieved from underlying search engines, but providing the rank list of relevant documents with respect to the user query is one of the most challenging tasks for any Meta-search engine. In this paper, we propose an algorithm for merging the results of retrieved documents from underlying search engines. The proposed algorithm makes use of genetic algorithm to find the single rank list of retrieved documents for the user query. The results show that the retrieved documents of rank list are more comprehensive and relevant. The results also motivate to explore and optimize the information retrieval in Meta-search engines.