文章基本信息

标题：Applying Genetic Algorithms to Information Retrieval Using Vector Space Model
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作者：Laith Mohammad Qasim Abualigah ; Essam S.Hanandeh
期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2015
卷号：5
期号：1
DOI：10.5121/ijcsea.2015.5102
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the informationretrieval process, and to increase the efficiency of the optimal information retrieval in order to meet theusers’ needs and help them find what they want exactly among the growing numbers of availableinformation. The improvement of adaptive genetic algorithms helps to retrieve the information needed bythe user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, theresearcher explored the problems embedded in this process, attempted to find solutions such as the way ofchoosing mutation probability and fitness function, and chose Cranfield English Corpus test collection onmathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also usedcosine similarity and jaccards to compute similarity between the query and documents, and used twoproposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed atevaluating the effectiveness of results according to the measures of precision and recall. Finally, the studyconcluded that we might have several improvements when using adaptive genetic algorithms.
关键词：Information Retrieval; genetic Algorithm; Adaptive genetic Algorithm; Vector Space Model; Precision;Recall