Clonal selection algorithm based on multi-memory mechanism with applications to pattern recognition
基于Multi-memory机制的克隆选择算法及其在模式识别中的应用

Currently in the research of artificial immune system, the clonal selection principle is commonly used in designing immune recognition algorithms. This paper described the general framework of CLONALG which was a clonal selection algorithm proposed by Castro, and pointed out that the convergence could not be guaranteed when it was applied to large-scale pattern recognition problems. A multi-memory mechanism was designed, based on which a new immune algorithm referred to as MCA was proposed and applied to pattern recognition problems. A new formula was proposed to calculate the mutation probability, which was a key factor in training memory antibodies. The results of experiments show that both the generalization capability and the recognition accuracy of MCA are better than that of CLONALG, and the MCA can be effectively applied to large-scale problems.