Building of an information retrieval system based on genetic algorithms

Abstract

In an information retrieval system (IRS) the query plays a very important role, so the user of an IRS must write his query well to have the expected result. In this paper, we have developed a new genetic algorithm-based query optimization method on relevance feedback for information retrieval. By using this technique, we have designed a fitness function respecting the order in which the relevant documents are retrieved, the terms of the relevant documents, and the terms of the irrelevant documents. Based on three benchmark test collections Cranfield, Medline and CACM, experiments have been carried out to compare our method with three well-known query optimization methods on relevance feedback. The experiments show that our method can achieve better results.