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

作     者：丁卫平 王建东 张晓峰 管致锦 Ding Weiping;Wang Jiandong;Zhang Xiaofeng;Guan Zhijin

作者机构：南京大学计算机软件新技术国家重点实验室南京210093 南通大学计算机科学与技术学院南通226019 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 南京航空航天大学计算机科学与技术学院南京210094 

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) 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2016年第32卷第4期

页      码：432-438页

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 ***, 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 ***, 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 ***, 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.

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

学科分类：08[工学] 081202[081202] 0812[工学-测绘类] 

核心收录：

D　O　I：10.3969/j.issn.1003-7985.2016.04.007

馆 藏 号：203210196...