By inspiration of the granular evolutionary algorithm

a Granular Agent Evolutionary algorithm for Classification (GAEC) is proposed.The method uses the granular agent to denote the set of examples that have similar attributions and the knowledge base guides the evolutionary of granular agent.Also some granular evolutionary operators are designed for classification problem.Assimilation operator

exchange operator

and differentiation operator reflect the competitive

cooperative and self-learning ability of agent respectively.Finally

some classification rules are extracted from granular agents by some strategies to forecast the sort of new data.Empirical studies show that the algorithm has a good classification prediction

and only need a small price for the training time.In most UCI datasets

the performance of GAEC is better than G-NET

OCEC and C4.5

which have good performance.