We report here on preliminary work on the use of evolutionary computing techniques which aims to improve Boolean information retrieval system performance through relevance feedback. There are many evolutionary techniques in computing, such as neural networks and genetic algorithms. One specific form of genetic algorithm technique has been used in our study: that of genetic programming. Terms from relevant documents are used to randomly create Boolean queries. Boolean queries are thought of as genetic programming organisms and, as such, are used for breeding to produce new organisms. Organisms which perform well, in terms of how good they are at retrieval, are given a better chance of being selected for breeding, with the result being that the overall fitness of the organisms improve to some extent. The aim is to develop the best Boolean query for an information need, given a small corpus of test documents, and then to use that query on the full collection to retrieve yet more relevant documents.
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