Granular Agent Evolutionary Algorithm for Classification
Liu Fang · Dianzi xuebao · 2009
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.