Efficient Algorithm for Web Search Query Reformulation Using Genetic Algorithm

A typical web user imposed small and vague queries onto web based search engines, which requires higher time for query formulation. In this paper, a nature inspired optimization approach on term graph is employed in order to provide query suggestion by assessing the similarity. Term graph is simulated according to the pool of relevant documents of user query. The association among terms graphs is based on similarity and will be act as fitness values for genetic algorithm (GA) approach, which converges by deriving query reformulations and suggestions. Each user interactions with the search engine is a considered as an individual chromosome and larger pool help in convergence for significant reformulations. Proposed algorithmic solution select optimal path and extracts the most relevant keywords for an input search query's reformulation. The query user will select one the suggested reformulated query or query terms. The optimization performance of the proposed method is illustrated and compared with different optimization techniques, e.g. ACO, PSO, ABC.