An Efficient Information Retrieval System Using Evolutionary Algorithms

نویسندگان

چکیده

When it comes to web search, information retrieval (IR) represents a critical technique as pages have been increasingly growing. However, users face major problems; unrelated user query retrieved documents (i.e., low precision), lack of relevant document recall), acceptable time, and minimum storage space. This paper proposed novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The IRS includes three main stages; the first stage indexing method) is preprocessing, which consists two steps: dataset reading (ADIM), resulting in set tables. second searching algorithm produce words or keywords related retrieving. third algorithm) steps. modified genetic (MGA) new fitness functions using cross-point operator dynamic length chromosomes adaptive function culture (CA). system ranks most by adding simple parameter (?) guarantee convergence solution, retrieving user’s integrating MGA CA achieve best accuracy. was simulated free called WebKb containing Worldwide Webpages computer science departments at multiple universities. composed 8280 HTML-programed semi-structured documents. Experimental results evaluation measurements showed 100% average precision 98.5236% recall for 50 test queries, while response time 00.46.74.78 milliseconds 18.8 MB memory space indexing. work outperforms all literature, comparatively, representing remarkable leap studied field.

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ژورنال

عنوان ژورنال: Network

سال: 2022

ISSN: ['2673-8732']

DOI: https://doi.org/10.3390/network2040034