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.
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