Gray编码对族群进化算法性能的改进

族群进化算法（EGEA）利用族群机制进行群体结构调控。在基于二进制编码的群体中,个体间编码的差异性被作为族群聚类的标准。由于自然二进制编码所存在的Hamming悬崖问题易影响族群聚类的准确性,从而降低EGEA的搜索效率,因此提出利用Gray编码连续个体间编码只有一位不同的特点来改进族群聚类的精度。针对典型多维函数的仿真实验表明,基于Gray编码的族群聚类过程可显著提高EGEA的收敛速度和解的精度。     

面向多层次知识表达的贝叶斯分类模型研究

提出多模式贝叶斯分类算法,由变量值之间的条件独立和条件相关性推断因果关系,根据每个完整随机样本而非整个样本空间构造子模式.结合局部计算近似推理进行概率密度和条件概率分布估计,在此基础上采用后离散化策略自动确定连续变量边界.在UCI机器学习数据集上的实验结果证明了该算法的合理性和有效性.     

Auto-generation of IEC standard PLC code using t-MPSG

The objective of this paper is to reduce the development time of a PLC (Programmable Logic Controller) by automating the task of code generation. For this purpose, we applied t-MPSG (Timed-Message Based Part State Graph). The t-MPSG is an extended finite state automata used to model and generate an execution module for a real-time shop floor controller system. In our proposed method, t-MPSG is used to model the formal specification of the controller system that can be translated into textual structure. After the verification of the t-MPSG model, it can be used as an input to the plc-builder tool. The plc-builder tool is an extended version of a conventional MPSG simulator. It can be used to translate the textual structure of the t-MPSG into an IEC standard PLC code. Finally, the generated code can be downloaded to a PLC emulator or a PLC device for the purpose of simulation and execution. The similarity in the hierarchical structure of the t-MPSG and the IEC standard PLC program has made it convenient to transform from one form to another. Furthermore, an illustration of the methodology to auto-generate IEC standard PLC code using t-MPSG is explained with a suitable example. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. This work was partially supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD080042AD). Devinder Thapa is a Postdoc Research Fellow in the Department of Industrial & information systems at Ajou University, Korea. He completed his Ph.D. from Ajou University in Industrial and Information Systems Engineering. His area of research is related to manufacturing automation and intelligent decision support systems. Chang Mok Park is a Professor in the Department of Technology & Systems Management at Induk Institute of Technology. He completed his Ph.D. in 2002 from Ajou University in Industrial Engineering. His research interest is related to manufacturing optimization, discrete event system simulation and signal analysis. Sang C. Park is an Associate Professor in the Department of Industrial & Information Systems Engineering at Ajou University. He received his B.S., M.S., and Ph.D. degrees from KAIST in 1994, 1996, and 2000, respectively, all in Industrial Engineering. His research interests include geometric algorithms in CAD/CAM, process planning, engineering knowledge management, and discrete event system simulation. Gi-Nam Wang is the Head and a Professor in the Department of Industrial & Information Systems Engineering at Ajou University, Korea. He completed his Ph.D. in 1992 from Texas A&M University, in Industrial Engineering. He has worked as Visiting Professor at University of Texas at Austin during 2000–2001. His area of research is related to Intelligent Information & manufacturing systems, system integration & automation, e-Business solutions and image processing.     

遗传编程在符号回归中的应用

遗传编程是一种新型的搜索优化技术,文章介绍了遗传编程的基本原理,以及遗传编程的算法设计及其实现的几个关键问题,并研究了基于遗传编程方法的符号回归。与传统回归方法相比,该方法得到的拟合函数更精确,具有更广泛的适用性。文中通过对一个函数进行符号回归验证,说明此方法合理可行。     

一个新的证明网定义及合理性

本文给出一个新的线性逻辑的证明网的定义并证明了所定义的证明网是线性逻辑的自然推理．和Ｇｉｒａｒｄ的原定义相比,使用本文给出的定义来判定一个证明结构是否为证明网的时间复杂度为Ｏ（ｎ＊ｎ）,并且在证明所定义的证明网是可矢列化时更加自然和简单．     

融合指标分组的高维混合多目标进化优化

高维混合多目标优化问题因包含多个不同类型指标,目前尚缺乏有效求解该问题的进化优化方法。提出一种基于目标分组的高维混合多目标并行进化优化方法。采用深度学习神经网络预测种群隐式性能指标;基于指标相关性,将高维混合多目标优化问题分解为若干子优化问题;采用多种群并行进化算法,求解分解后的每一子优化问题,并基于各子种群的非被占优解构建外部保存集;采用聚合函数对外部保存集个体进一步优化,得到Pareto最优解集。在室内布局优化问题中验证所提方法,实验结果表明,所提方法的Pareto最优解在收敛性、分布性以及延展性等方面均优于对比方法。     

对一种白盒SM4方案的差分计算分析

传统密码算法的安全性建立在黑盒攻击模型下. 在这种攻击模型下, 攻击者只能获取密码算法的输入输出, 而无法得知密码算法运行时的内部细节. 近年来白盒攻击模型的概念被提出. 在白盒攻击模型下, 攻击者既可以获取密码算法的输入输出, 也可以直接观测或更改密码算法运行时的内部数据. 为保证已有密码算法在白盒攻击环境下的安全性, 在不改变其功能的基础上通过白盒密码技术对其进行重新设计被称为已有密码算法的白盒实现. 研究白盒实现方案的设计与分析对于解决数字版权管理问题具有重要意义. 近年来, 出现了一类针对白盒实现方案的旁信道分析方法. 这类分析手段只需要知道很少白盒实现方案的内部细节, 却可以提取到密钥, 因此是一类对现有白盒实现方案具有实际威胁的分析手段. 对现有白盒实现方案进行此类分析对于确保方案安全性具有重要现实意义. 此类分析方法中的典型代表是基于差分功耗分析原理的差分计算分析. 基于差分计算分析, 对白-武白盒SM4方案进行了安全性分析. 基于对GF(2)上n阶均匀随机可逆矩阵统计特征的研究结果, 提出了一种改进型差分计算分析(IDCA), 可以在分析成功率几乎不变的前提下显著提升分析效率. 结果表明, 白-武白盒SM4方案在面对差分计算分析时不能保证安全性, 必须对其进行进一步改进使之满足实际应用场景下的安全性需求.     

An enhanced preference-inspired co-evolutionary algorithm using orthogonal design and an ε-dominance archiving strategy

The concept of co-evolution of preferences and candidate solutions has proven effective for many-objective optimization. One realization of this concept, namely preference-inspired co-evolutionary algorithms using goal vectors (PICEA-g), is found to outperform many state-of-the-art multi-objective evolutionary algorithms for many-objective problems. However, PICEA-g is susceptible to unevenness in the solution distribution. This study seeks to tackle this issue and to improve the performance of PICEA-g further. Two established strategies are incorporated into PICEA-g: (i) an adaptive ε-dominance archiving strategy which is applied to obtain a set of well spread solutions online; and (ii) the orthogonal design method which is used to initialize candidate solutions. The improved algorithm, denoted as aε-ODPICEA-g, shows a better performance than PICEA-g on both 2- and 7-objective benchmark problems as well as a real-world problem.     

封闭空间爆炸载荷特性研究

封闭空间爆炸载荷主要包含瞬态冲击波和持续时间较长的准静态超压。为了研究封闭空间爆炸载荷特性,基于FORTRAN平台,采用三阶WENO有限差分格式编写了爆炸波高精度三维数值计算程序。应用Sod激波管、双爆轰波碰撞等经典算例验证了所开发数值程序的可靠性。在封闭空间内炸药爆炸波数值计算的基础上,基于冲量等效原则提出封闭空间内爆炸载荷简化模型,理论推导给出准静态超压峰值计算公式并通过数值计算结果验证了该公式的可靠性。开发的高精度爆炸波三维数值计算程序及提出的简化载荷模型可用于封闭空间内爆炸载荷的快速计算,为工程抗爆结构设计提供载荷输入。