GEP Classification Based on Clonal Selection and Quantum Evolution
基于克隆选择和量子进化的GEP分类算法

Gene Expression Programming based Classification algorithm has shown good classification accuracy,however, it often falls into the local optimums and needs long time searching. In order to further improve the classification power of GEP, clonal selection and quantum evolution were introduced into GEP. A novel approach called C1onalQuantum-GEP was proposed. After affecting the search direction and evolution ability of the antibody population through the updating and exploring of the quantum population, and keeping the best results in the memory pool, this approach gets more pop- ulation diversity, better ability of global optimums searching, and much faster velocity of convergence. Experiments on several benchmark data sets demonstrate the effectiveness and efficiency of this approach. Compared with basic GEP, C1onalQuantum-GEP can achieve better classification results with much smaller scale of the population and much less evolutionary generation.