文章基本信息

标题：AN EFFICIENT ADAPTIVE GENETIC ALGORITHM FOR VECTOR SPACE MODEL
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作者：WAFA' ALMA'AITAH ; KHALED ALMAKADMEH
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：71
期号：2
出版社：Journal of Theoretical and Applied
摘要：In this study, we propose to use an Adaptive Genetic Algorithm (AGA) aimed to enhance the performance of information retrieval under Vector Space Model (VSM) in both (Cosine and Dice similarity). Using the algorithm is aimed to improve the quality of the results of user's query and generate improved queries that fit searcher�s needs. Furthermore, we investigate and evaluate different fitness functions that reduce the search space and reduce the number of iterations needed to generate an optimized query. Traditional Genetic Algorithms (GA) use fixed values for crossover and mutation operators; and such values remained fixed during the execution of the algorithm. The adaptive genetic algorithm uses crossover and mutation operators with variable probability; which allows for faster attainment of improved query results. The proposed approach is verified using (242) proceedings abstracts (in Arabic) collected from the Saudi Arabian National conference.
关键词：Information Retrieval; Adaptive Genetic Algorithm; Vector Space Model; Query Optimization.