Abstract—The rapid growth of web pages available on the
Internet recently, searching relevant and up-to-date
information has become a crucial issue. Information retrieval is
one of the most crucial components in search engines and their
optimization would have a great effect on improving the
searching efficiency due to dynamic nature of web it becomes
harder to find relevant and recent information. That’s why
more and more people begin to use focused crawler to get
information in their special fields today. Conventional search
engines use heuristics to determine which web pages are the
best match for a given keyword. Earlier results are obtained
from a database that is located at their local server to provide
fast searching. However, to search for the relevant and related
information needed is still difficult and tedious. This paper
presents a model of hybrid Genetic Algorithm -Particle Swarm
Optimization (HGAPSO) for Web Information Retrieval. Here
HGAPSO expands the keywords to produce the new keywords
that are related to the user search.
Index Terms—Genetic algorithm, information retrieval
system, particle swarm optimization.
The authors are with the Priyadarshini Institute of Engineering and
Technology, Nagpur, India (e-mail:priyas1586@yahoo.co.in,
harshleena23@rediffmail.com).
[PDF]
Cite:Priya I. Borkar and Leena H. Patil, "Web Information Retrieval Using Genetic
Algorithm-Particle Swarm Optimization," International Journal of Future Computer and Communication vol. 2, no. 6, pp. 595-599, 2013.
