Rough Set and Genetic Programming

Abstract

A methodology for using Rough Set for preference modeling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on Rough Sets theory combined with Genetic Programming algorithm. Genetic Programming belongs to the most newly te-hniques in applications of Artificial Intelligence. Rough Set Theory, which emerged about twenty years ago, is nowadays rapidly developing branch of Artificial Intelligence and Soft Computing. At the first glance the two methodologies we talk about have not in common. Rough Sets construct representation of knowledge in terms of attributes, semantic decision rules, etc. On the contradictory, Genetic Programming attempts to aut-omatically create computer programs from a high-level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both approaches into one combined system. The challenge is to get as much as possible from this association.