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 set combined with genetic programming. Genetic programming belongs to the most new techniques in applications of artificial intelligence. Rough set theory, which emerged about 20 years back, is nowadays a rapidly developing branch of artificial intelligence and soft computing. At the first glance, the two methodologies that we discuss are not in common. Rough set construct is the representation of knowledge in terms of attributes, semantic decision rules, etc. On the contrary, genetic programming attempts to automatically 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 the approaches into a combined system. The challenge is to obtain as much as possible from this association.
Combines rough set theory and tree-based GP to build rule sets/decision trees for data. Lower level representation and mutation operators not well defined (especially in the situations where it compares values against numerical information)
< RefGonzalezFernandez2000 | ReferencesFound | RefHurleyEtAl1995 >