Genetic programming for generating prototypes in classification problems

We propose a genetic programming based approach for generating prototypes in a classification problem. In this context, the set of prototypes to which the samples of a data set can be traced back is coded by a multitree, i.e. a set of trees, which represents the chromosome. Differently from other approaches, our chromosomes are of variable length. This allows coping with those classification problems in which one or more classes consist of subclasses. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.