Grouping Character Shapes by Means of Genetic Programming

In the framework of an evolutionary approach to machine learning, this paper presents the preliminary version of a learning system that uses Genetic Programming as a tool for automatically inferring the set of classification rules to be used by a hierarchical handwritten character recognition system. In this context, the aim of the learning system is that of producing a set of rules able to group character shapes, described by using structural features, into super-classes, each corresponding to one or more actual classes. In particular, the paper illustrates the structure of the classification rules and the grammar used to generate them, the genetic operators devised to manipulate the set of rules and the fitness function used to match the current set of rules against the sample of the training set. The experimental results obtained by using a set of 5,000 digits randomly extracted from the NIST database are eventually reported and discussed.