Co-evolutionary cloud-based attribute ensemble multi-agent reduction algorithm  

基于协同进化云的属性集成多代理约简算法（英文）

作　　者：丁卫平[1,2,3] 王建东[4] 张晓峰[2] 管致锦[2] 

机构地区：[1]南京大学计算机软件新技术国家重点实验室,南京210093 [2]南通大学计算机科学与技术学院,南通226019 [3]南京理工大学高维信息智能感知与系统教育部重点实验室,南京210014 [4]南京航空航天大学计算机科学与技术学院,南京210094

出　　处：《Journal of Southeast University(English Edition)》2016年第4期432-438,共7页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.61300167);the Open Project Program of State Key Laboratory for Novel Software Technology of Nanjing University(No.KFKT2015B17);the Natural Science Foundation of Jiangsu Province(No.BK20151274);Qing Lan Project of Jiangsu Province;the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(No.JYB201606);the Program for Special Talent in Six Fields of Jiangsu Province(No.XYDXXJS-048)

摘　　要：In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.为提高属性约简算法处理含噪音和不确定大数据的性能,提出了一种基于协同进化云的属性集成多代理约简算法(CCAEMR).该算法首先基于MapReduce机制设计协同进化云框架,将整个种群分解成多个具有自适应规模的协同进化子种群,通过子种群的共享奖酬来加速属性约简实现.然后,构造了一种协同精英优化的多代理集成策略,确保划分的子种群能够充分探索交叠属性子集之间的相关性和相互依赖性,且具有较强的抗噪音性能,这些代理能保持在稳定的精英地区且取得了最佳收益.实验结果表明:所提出的CCAEMR算法在解决大规模和不确定复杂噪音数据的属性约简时具有更好的效率和适用性.

关 键 词：co-evolutionary elitist optimization attribute reduction co-evolutionary cloud framework multi-agent ensemble strategy neonatal brain 3D-MRI 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]