基于进化算法的约束处理技术

约束优化问题是科学和工程应用领域经常会遇到的一类数学规划问题,因而对其研究具有十分重要的理论和实际意义．进化算法是一种模拟自然进化过程的全局优化方法。综述基于进化算法的约束处理技术研究现状,提出几个值得关注的研究问题。     

基于演化算法的实因式分解

针对线性定常闭环系统特征方程的求根问题，提出了一种基于演化算法的实因式分解法。该法先将实因式分解问题转化为数值优化问题，再用基于混合杂交与间歇变异的演化算法求解该优化问题。数值实验结果表明了所提方法的有效性。     

进化算法时间复杂度估算的一般流程

进化算法的时间复杂度分析是当前进化计算领域研究的热点难题。通过总结最新进化算法理论研究,介绍了进化算法时间复杂度分析方法,并讨论了分析关键之处:距离函数的确定方法。最后,给出了进化算法时间复杂度分析的一般流程,且以分析范例演示了方法的执行过程。     

关于多目标演化算法的策略分析

基于Pareto排序算法的多目标演化算法是多目标演化算法所采用的重要方法,本文叙述了多目标演化算法(MOEAs)的有关概念,在分析已有算法的一些性能和特征的基础上,结合演化算法的有关概念,重点基于Pareto排序算法分析了影响多目标演化算法性能的两大方面:求解过程中解集合的多样性、均匀性分布的保持与维护以及解的收敛性,分析了MOEAs设计中需要注意的策略问题以及今后研究的重点.     

增加搜索能力的多目标进化算法

提出了一种增加搜索能力的多目标进化算法。该算法是针对如何收敛到真正的Pareto最优集进行处理的。在自适应变异步长进化策略的基础上,引入变异率的概念,使得该算法在进化前能进行全局搜索,而在进化后期进行局部调节,使得算法能够快速的收敛到真正的Pareto最优集。仿真实验表明该算法的有效性。     

进化算法及进化理论初探

从进化法则和遗传学的角度出发,讨论了进化算法的生物学原理,找到了其充分的生物学依据,参照自然选择机制,提出了进化算法中的随机选择和竞争性选择机制,最后,总结了进化算法的特点,对其存在问题和发展方向作了综述。     

Asynchronous parallel evolutionary algorithms for constrained optimizations

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. National Laboratory for Parallel and Distributed Processing Foundation item: Supported by the Natonal Natural Science Foundation of China (No. 70071042, 50073043), the National 863 Hi-Tech Project of China (No. 863-306-ZT06-06-3) and the National Laboratory for Parallel and Distributed Processing. Biography: Kang Li-shan (1934-), male, Professor, research interests: parallel computing and evolutionary computation.     

Drift analysis in studying the convergence and hitting times of evolutionary algorithms: An overview

This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with Markov chains or supermartingales. Then the drift conditions which guarantee the convergence of evolutionary algorithms are described. And next the drift conditions which are used to estimate the hitting times of evolutionary algorithms are presented. Finally an example is given to show how to analyse hitting times of EAs by drift analysis approach. Foundation item: Supported by Engineering and Physical Science Research Courcil (GR R52541 01) and State Laboratory of Software Engineering Foundation item: Supported by Engineering and Physical Science Research Courcil (GR R52541 01) and State Laboratory of Software Engineering Biography: He Jun( 1967-), male, visiting fellow at State Key Lab of Software Engineering, Ph D. , research direction: computational intelligence     

多维O-1背包问题的混合遗传算法

将贪婪法和遗传算法相结合，设计了一种价值密度，提出了一种求解多维0-1背包问题的混合遗传算法。经实例证明，该方法能较好地解决多维0-1背包问题并较简单遗传算法有较好的改善。     

A generic design model for evolutionary algorithms

A generic design model for evolutionary algorithms is proposed in this paper. The model, which was described by UML in details, focuses on the key concepts and mechanisms in evolutionary algorithms. The model not only achieves separation of concerns and encapsulation of implementations by classification and abstraction of those concepts, it also has a flexible architecture due to the application of design patterns. As a result, the model is reusable, extendible, easy to understand, easy to use, and easy to test. A large number of experiments applying the model to solve many different problems adequately illustrate the generality and effectivity of the model. Foundation item: Supported by the National Natural Science Foundatron of China (70071042, 60073043, 60133010) Biography: He Feng (1974-), male, Ph. D candidate, research direction evolutionary computation, software engineering.     

遗传算法的改进及其在方程组求解中应用

选择、交叉和变异是遗传算法的几个主要操作算子,它们构成了遗传操作。对遗传操作提出了改进方案、即对于交换操作：如果两个子代的适应度均比父代大就交换,如果子代的适应度一个比父代大而另一个比父代小则保留大的子代而还原小的子代为父代．如果子代的适应度均比父代小则取消此次的交换。变异操作中对每个父代的多个位置逐个变异．如果子代的适应度比父代大则变异,否则不变异。通过解线性方程组和非线性方程组证明丁该方法能够使得遗传始终向着理想的方向,避免了算法陷入死循环,并且收敛速度非常快。     

两种智能值排序启发式研究

为提升约束满足问题求解效率, 对最受推崇的智能值排序启发式Look-ahead 和Survivors-first 进行深入研究。比较两种值排序启发式在常规和自适应两种环境下的效率表现。结果显示, 在多数问题类上, 常规情况下Survivors-first效果更好, 而在自适应环境下效率有所下降; 在不同环境下使用不同启发式可提升约束满足问题求解效率。     

Financial data modeling by using asynchronous parallel evolutionary algorithms

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends. Biography: Wang Chun ( 1969-), male, Ph. D candidate, research direction: e-business.     

A new evolutionary algorithm for function optimization

A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory. Foundation item: Supported by the National Natural Science Foundation of China (No. 69635030), National 863 High Technology Project of China, the Key Scientific Technology Development Project of Hubei Province. Biography: GUO Tao(1971-), male, Ph D, research interests are in evolutionary computation and network computing.     

求解非线性优化问题改进的进化规划算法

针对带有边界变量的非线性优化问题,提出了一种改进的进化规划算法.该算法首先把每个个体看作为带有不同质量的粒子,根据目标函数值定义个体的质量.基于聚类思想,选取一定数量的点,然后利用选取的点分别求出于每个点相对应的重心,以每个点与其重心的连线方向为变异方向.最后把该方法应用到几个典型数值例子中,并与基本进化算法进行比较,数值结果表明算法是可行的、有效的.     

列车控制问题的计算分析及自适应算法

在对原有算法分析的基础之上指出其导致计算不能执行下去的原因并给出参数的下界,提出一种自适应的算法,并得到满意的实验结果.     

对一类背包密码体制攻击中的遗传算法欺骗性分析

利用模式分析法从理论上说明了MH背包密码体制分析中存在严重的欺骗性,从而得出该算法的不可行性。     

基于演化算法的学习路线生成与优化

在分析了智能教学系统中传统学习路线生成算法的基础上,提出了一种基于演化算法的学习路线生成与优化方法,经分析与研究证明,该算法对有效地提高教学系统的智能化程度起到了相应的作用。