Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Over the years, EAs have been successfully applied to many classification problems. In this paper, we propose to demonstrate the performance of an improved evolutionary algorithm for synthesizing classifiers in supervised data scenarios. This approach generates an arithmetic expression DAG (Directed Acyclic Graph) for each training class in order to adjust each test class to one of them. We compare our approach with well-known machine learning methods, such as SVM and KNN. The performance of the improved algorithm for evolving classifiers is competitive with respect to the solution quality.