Ranking Algorithm to Optimize the Retrieval Process Using Genetic Algorithm
The online information is growing at a fast rate in recent years. The repository which we called World Wide Web has diverse information that satisfies the needs of the user and to access the derived information the structure of the internet should be analyzed properly. To access the needed information various ranking algorithms are proposed that act as the backbone for the information retrieval system. To optimize the retrieved information, Nature-inspired evolutionary measures can be used. Genetic Algorithm is used here that handle efficiently the complex environment of the web and can be used for efficient search. The paper proposes a gRank algorithm that makes use of a genetic algorithm for ranking the results of the query. By applying certain conditions on the web pages they are clustered together and thereby selection is done followed by crossover and finally mutation is applied. The working process of gRank and comparison of gRank with the PageRank algorithm is illustrated in the paper. The given approach helps in optimizing the retrieved information in minimum time duration by following a limited set of iterations.