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） algorith...
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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.
能够提供更强计算能力的多核处理器将在安全关键系统中得到广泛应用,但是由于现代处理器所使用的流水线、乱序执行、动态分支预测、Cache等性能提高机制以及多核之间的资源共享,使得系统的最坏执行时间分析变得非常困难.为此,国际学术界提出时间可预测系统设计的思想,以降低系统的最坏执行时间分析难度.已有研究主要关注硬件层次及其编译方法的调整和优化,而较少关注软件层次,即,时间可预测多线程代码的构造方法以及到多核硬件平台的映射.提出一种基于同步语言模型驱动的时间可预测多线程代码生成方法,并对代码生成器的语义保持进行证明;提出一种基于AADL(architecture analysis and design language)的时间可预测多核体系结构模型,作为研究的目标平台;最后,给出多线程代码到多核体系结构模型的映射方法,并给出系统性质的分析框架.
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