Exploring genetic programming for modeling character shape
Angelo Marcelli · 2002
In the framework of an evolutionary approach to machine learning, this paper proposes to use genetic programming as a tool to implement a learning module whose purpose is that of finding the set of prototypes to be used by a handwritten character recognition system. After discussing the rationale behind this choice, we describe the structural character shape representation adopted and the coding scheme for transforming such a two dimensional representation into a vector-based one, especially suitable for genetic programming. Then, the basic principles according to which the approach has been designed are presented, together with the genotype's structure, the fitness function and the genetic operators devised to deal with the problem at hand. The results of a preliminary experiment performed on a standard database of handwritten characters are eventually reported.