基于克隆选择和量子进化的GEP分类算法

摘要

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 ClonalQuantum-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 population 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, ClonalQuantum-GEP can achieve better classification results with much smaller scale of the population and much less evolutionary generation.%基于基因表达式编程(GEP)的分类算法具有较高的精度,但易陷入局部最优,且搜索时间长.为进一步提高GEP分类算法的分类能力,提出了基于克隆选择和量子进化的GEP分类算法——ClonalQuantum-GEP.该算法通过量子种群的更新和探测影响抗体种群的搜索方向和进化能力,并通过记忆池保持最优解,使其具有更好的种群多样性、更强的全局寻优能力和更快的收敛速度.在几个标准数据集上的实验验证了算法的有效性.与基本的GEP算法相比,ClonalQuantum-GEP能以较小的种群规模和较少的进化代数获得较理想的分类效果.