基于克隆选择和量子进化的GEP分类算法  

GEP Classification Based on Clonal Selection and Quantum Evolution

作　　者：王卫红[1] 杜燕烨[1] 李曲[1,2] 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023 [2]浙江大学计算机学院,杭州310038

出　　处：《计算机科学》2011年第10期236-239,262,共5页Computer Science

基　　金：国家自然科学基金(60873033);浙江省自然科学基金(R1090569);浙江省科学技术厅项目(2009C31108)资助

摘　　要：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。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 basicGEP,ClonalQuantum-GEP can achieve better classification results with much smaller scale of the population and much less evolutionary generation.

关 键 词：基因表达式编程(GEP) 克隆选择 量子进化 分类 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]