Operators of Genetic Page-Rank Algorithm

Abstract

Genetic algorithm operators are techniques used to evolve solutions, primarily including selection (choosing fit parents), crossover (recombining parents to create offspring), and mutation (randomly altering offspring to maintain diversity). These operators iteratively improve a population of solutions, mimicking natural selection to solve complex optimization problems. Data mining is extracting and automatic discovering the web based information has been used as web mining. It is one of the most universal and a dominant application on the Internet and it becomes increasing in size and search tools that combine the results of multiple search engines are becoming more valuable. But, almost none of these studies deals with genetic page rank algorithm (GPRA), where GPRA is one of the evolutionary methods with graph structure. GPRA was designed to both increase the effectiveness of search engine and improve their efficiency. GPRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GPRA. The reduced number of hyperlinks provided by GPRA in the final generation consists of only the most similar hyperlinks with respect to the query. But, the end user’s not satisfied fully. To improve the satisfaction of user by using Page rank algorithm to measure the importance of a page and to prioritize pages returned from a GPRA. It will reduce the user’s searching time. Page Rank algorithm works to allocate rank for filtered links based on number of keyword occurred in the content.