Feature selection with link-like agent genetic algorithm combining multi-criteria
基于多准则的链式智能体遗传算法用于特征选择

According to low precision and over early convergence problems,the paper proposed a new genetic algorithm: link-like agent genetic algorithm(LAGA) and thereby proposed new method for feature selection with LAGA combining multi-criteria(MC).LAGA introduced link-like agent structure,competition selection,adaptive crossover and adaptive mutation,so it could obtain more precise search result.MC could judge the feature bits of the feature subset obtained through single criterion,thereby,obtained final feature subset to get more comprehensive and more stable result.The empirical results show that LAGA can get more precise search result;the feature subset obtained through LAGA MC has better classification rate and more stable classification result.