Improving the Effectiveness of Information Retrieval System Using Adaptive Genetic Algorithm

Abstract

Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic algorithm. Genetic algorithm started to be applied in information retrieval system in order to optimize the query by genetic algorithm, a good query is a set of terms that express accurately the information need while being usable within collection corpus, the last part of this specification is critical for the matching process to be efficient, that is why most research efforts are actually put toward the query improvement. We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean model, and Language model in information retrieval (IR), the algorithm used crossover and mutation operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those, and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and crossover probability; this allows faster attainment of better solutions. The paper has been tested using 242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.