Impact of Different Selection Strategies on Performance of GA Based Information Retrieval
Anubha Jain, Swati V. Chande · International Journal on Computational Science & Applications · 2015
As the information proliferates, searching for relevant information has become a primary task.Searching or Information retrieval (IR) aims to help the users in organising as well as retrieving those documents from the documentary collection which are most likely to satisfy information needs of the user.An optimal Information Retrieval System (IRS) is one which retrieves only those documents from the document database which are pertinent to user's information needs, while excluding documents that are not relevant.Genetic Algorithm is described by higher likelihood of finding good solutions to large and complex problems of IR optimisation.The performance of Genetic Algorithm depends upon the decision of underlying operators used namely selection, crossover and mutation.A GA-based algorithm IRIGA (Information Retrieval Improvement using Genetic Algorithm) is developed to improve the performance of Information Retrieval System.This paper presents a comparison of performance of IRIGA when different selection methods are used.The results are analysed by conducting experiments keeping the rest of the GA parameters as constant and varying only the selection strategy.