Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Large test collection experiments on an operational, interactive system: Okapi at TREC
TREC-2 Proceedings of the second conference on Text retrieval conference
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
Combining multiple evidence from different types of thesaurus for query expansion
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Query Optimization in Information Retrieval Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
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A new method for query expansion using genetic programming (GP) is proposed in this paper to enhance the retrieval performance of text information retrieval systems. Using a set of queries and retrieved relevant and nonrelevant documents corresponding to each query, GP tries to evolve a criteria for selecting terms which when added to the original query improve the next retrieved set of documents. Two experiments are conducted to evaluate the proposed method over three standard datasets: Cranfield, Lisa and Medline. In first experiment a formula is evolved using GP over a training set and is then evaluated over a test query set of the same dataset. In the second experiment, evolved expansion formula over a dataset is evaluated over a different dataset. We compared our method against the base probabilistic method in literature. Results show a higher performance in comparison with original and probabilistically expanded method.