A Technique for Improving Web Mining using Enhanced Genetic Algorithm

World Wide Web is growing at a very fast pace and makes a lot of information available to the public. Search engines used in the conventional methods to retrieve information on the web; however, the results of these engines are still capable of being refined and their accuracy is not high enough. One of the methods available for web mining is evolutionary algorithms, which do searches according to the users’ interests. A new genetic algorithm (GA) optimizes the important relationships among links on web pages with evolutionary algorithms. This paper presents a new method for classifying web documents based on the modified GA to find the best pages among the ones searched by engines. It also calculates, independently or dependently, the quality of pages by web page features. The proposed algorithm is complementary to the search engines. In the proposed method, after implementation of GA using the MATLAB 2013 software and a cross-over rate of 0.7 and a mutation rate of 0.05, the best and most similar pages are presented to the user. In the case of algorithm recurring, the result will not change