Immune Algorithm with Memory Coevolution

Abstract

Although various immune algorithms have been proposed by researchers to be put to use in engineering practice, these immune algorithms fail to take into account the impact of the complex relationship between the environment and the individual on the evolution of the individual. Therefore, the convergence rate of such algorithm can be slow in practical applications. The Memory Coevolution Immune Algorithm (MCIA) is proposed to overcome the above defect. The strategy of memory coevolution was proposed; the distance concentration and affinity function were defined. Based on the evaluation of the antibodies and utilizing the synergic evolution philosophy, the antibodies in the memory library are selected and crossed with the cloned antibodies according to the affinity value. As a result, the excellent antibody genes are spread among different antibodies. Meanwhile, on the basis of the full mutation, the other antibodies in the memory library are selected, and are crossed with the cloned antibodies. As a result the antibody genes with poor quality have been contained. The experiment shows that the adoption of memory coevolution mechanism in MCIA enhanced the algorithm's search capabilities.