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Introducing lateral thinking in search engines with interactive evolutionary algorithms

Nowadays, large medical databases consist of a collection of smaller databases, each on possibly different fields and using different formats, making it increasingly difficult to retrieve valuable information among the thousands of documents retrieved by a simple query. A new Evolutionary Learning Interactive Search Engine (ELISE) feeds on previous user requests to retrieve "alternative" documents that may not be returned by more conventional search engines, in a way that may recall "lateral thinking." Tests on the "Cystic Fibrosis Database" benchmark [1] prove 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.