Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Readings in information retrieval
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Query Optimization in Information Retrieval Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
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We present in this paper the design of ELISE, an interactive GP system for document retrieval tasks in very large medical databases. The components of ELISE have been tailored in order to produce a system that is capable of suggesting documents related to the query that may be of interest to the user, thanks to evolved profiling information. Tests on the "Cystic Fibrosis Database" benchmark [2] show that, while suggesting original documents by adaptation of its internal rules to the context of the user, ELISE is able to improve its recall rate.