A multiclass classifier using Genetic Programming

This paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). The proposed approach takes an integrated view of all classes when GP evolves. An individual of the population will be represented using multiple trees. The GP is trained with a set of N training samples in steps. A concept of unfitness of a tree is used in order to improve genetic evolution. Weak trees having poor performance are given more chance to participate in the genetic operations, and thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation operation. The approach is being demonstrated by experimenting on some datasets.