An Improved Memetic Algorithm for Web Search

In order to search a relevant data from World Wide Web, user use to submit query to search engine. Search engine returns combination of relevant and irrelevant results. This paper proposes a novel method based on Memetic Algorithm (MA) for searching the most relevant snippets in case of complex queries. The improved memetic algorithm (IMA) uses a hybrid-selection strategy to enhance the search result. Classical local search operators are combined for improvement in final output. Besides, the same chromosomes are modified to be different so that the population diversity is preserved and the algorithm kept from premature convergence. The performance of IMA is tested by comparing the result of search engine, basic Memetic and Improved Memetic Algorithm. Experimental results show that IMA could obtain superior solutions to the counterparts.