  Cluster-based information retrieval is one of the Information retrieval(IR)
tools that organize, extract features and categorize the web documents
according to their similarity. Unlike traditional approaches, cluster-based IR
is fast in processing large datasets of document. To improve the quality of
retrieved documents, increase the efficiency of IR and reduce irrelevant
documents from user search. in this paper, we proposed a (K-means) -
Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the
K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG
algorithms. K-means uses to cluster the population to k subpopulations then
take most clusters relevant to the query to manipulate in a parallel way by the
two levels of genetic parallelism, thus, irrelevant documents will not be
included in subpopulations, as a way to improve the quality of results. Three
common datasets (NLP, CISI, and CACM) are used to compute the recall,
precision, and F-measure averages. Finally, we compared the precision values of
three datasets with Genetic-IR and classic-IR. The proposed approach precision
improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the
NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28%
in CISI, and 34% in NLP.
