Search

Web search engines have become an integral part of the daily life of a knowledge worker, who depends on these search engines to retrieve relevant information from the Web or from the company's vast document databases. Current search engines are very fast in terms of their response time to a user query. But their usefulness to the user in terms of retrieval performance leaves a lot to be desired. Typically, the user has to sift through a lot of nonrelevant documents to get only a few relevant ones for the user's information needs. Ranking functions play a very important role in the search engine retrieval performance. In this paper, we describe a methodology using genetic programming to discover new ranking functions for the Web-based information-seeking task. We exploit the content as well as structural information in the Web documents in the discovery process. The discovery process is carried out for both the ad hoc task and the routing task in retrieval. For either of the retrieval tasks, the retrieval performance of these newly discovered ranking functions has been found to be superior to the performance obtained by well-known ranking strategies in the information retrieval literature. [ABSTRACT FROM AUTHOR]

The article focuses on the order based fitness function on genetic programming, based on the utility theory. Genetic algorithms and genetic programming are a set of artificial intelligence search algorithms based on evolutionary theory. Set of fitness functions are tested along with other existing order based fitness functions on the task of ranking function discovery for web search using genetic programming (GP). The experiments on a large Web corpus show that several of designs are very effective in guiding the GP search. Among the various fitness functions tested only four of them are recommended. These four all follow the order-preserving condition set in equation and work very well in the Web search context. The guidelines mentioned can help future researchers design better fitness functions of their own in their GA or GP applications. Results indicate that fitness functions is instrumental in performance improvement. The article presents recommendations on the design of fitness function for genetic based information retrieval experiments.

The Internet and corporate Intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. In this paper, we argue that the ranking strategy should be context specific and we propose a new systematic method that can automatically generate ranking strategies for different contexts based on Genetic Programming (GP). The new method was tested on TREC data and the results are very promising. [ABSTRACT FROM AUTHOR]