Using Evolution Programs to Learn Local Similarity Measures

The definition of similarity measures is one of the most crucial aspects when developing case-based applications. In particular, when employing similarity measures that contain a lot of specific knowledge about the addressed application domain, modelling similarity measures is a complex and time-consuming task. One common element of the similarity representation are local similarity measures used to compute similarities between the values of single attributes. In this paper an approach to learn local similarity measures by employing an evolution program— a special form of a genetic algorithm— is presented. The goal of the approach is to learn similarity measures that sufficiently approximate the utility of cases for given problem situations in order to obtain reasonable retrieval results.