Evolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the diagnosis and treatment of diseases from this vast data has become imperative. Association rule mining is an obvious choice for representing this previously hidden information as rules are simple to understand and infer.