One of the main keys in case-based reasoning system is the retrieval phase, where the most similar cases are retrieved by means of a similarity function. According to the problem, the similarity function must be selected and adapted depending on the characteristics and properties of the problem's domain. The goal of this article is to present a platform called BRAIN, which incorporates strategies based on different evolutionary approaches to design similarity functions ad hoc for a domain to be used in a case-based reasoning system. The strategies are based on Genetic Programming and Grammar Evolution approaches. Both are applied to different data sets to study the influence of their characteristic in the accuracy rate and in the execution time.
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