遗传算法在试题库智能组卷中的应用

通过比较国内常用的组卷算法,遗传算法能解决选题的盲目性,并能从群体中选择更满足条件的个体,具有很强的智能性,同时它能根据不同的环境产生不同的后代,具有动态性、自适应性,从而能满足题库不断变化的要求.通过对智能组卷问题及遗传算子的研究分析,结合实际问题建立有效的数学模型,能够根据该模型从题库中按照用户给出的试卷指标选取一组最优解.开发的组卷系统经使用表明该系统具有较好的实用性和高效性,也证实所设计算法的可行性.     

二进制编码遗传算法在试题组卷中的应用研究

采用基因段遗传算法解决多目标试题组卷优化问题是目前比较常用的组卷方法,但其存在适用题库规模较小,遗传算子约束条件多,收敛速度慢等缺点。采用多染色体并行遗传算法解决多目标试题组卷优化问题,就是按照不同的题型划分为多个染色体种群,然后根据每种题型的目标要求,并行进行遗传算法操作,将优化结果拟合成最终试卷。这种方法不仅目标控制灵活、方便、收敛速度快、而且适用规模较大的题库。     

有约束的三维RFID读写器网络规划方法

针对现有的读写器网络规划计算量大和优化困难的问题,基于微遗传算法,提出一种带有约束的网络规划方法,以优化无线射频识别读写器。运用空间交叉的方法解决种群数量较少导致的搜索能力退化问题,提高搜索能力。为降低迭代成本,校正方案包含了冗余读写器的消除操作,使得校正后的可行性染色体都是低成本染色体。实验结果表明,与典型遗传算法和粒子群优化算法相比,该方法的计算效率较高,在计算时间和标签覆盖率方面具有较大优势。     

改进遗传算法对医学题库组卷问题的应用研究

对含分支题的医学题库进行研究,并对遗传算法做了改进,提出了占位符编码方案、扩位交叉算子和重题优化策略.占位符编码方案能分段定长编码的同时累计各题型段的实际分支题量;扩位交叉算子能智能扩展落在分支题段的交叉点,避免因分支题段局部交叉而出现重题和实际分支题量与条件不符等情况;重题优化策略能快速替换重题,有效缩短组卷时间.仿真结果表明,改进的算法能适应不同题型,在不影响一般题型段抽取与进化的同时,精确控制分支题段的总分支题量和质量,是解决医学题库智能组卷问题的一种有效途径.     

基于主从式并行遗传算法的S盒优化算法

针对传统遗传算法优化高阶S盒中适应度评价值计算量相对大的问题,利用MPI+C环境结合主从式并行遗传算法框架来改进传统串行遗传算法,在MPP处理机——“深腾-1800”（联想）上进行测试,实验结果表明,运用此并行算法优化6阶、8阶的双射S盒,只在优化8阶的情况下能得到更好的加速比,从而验证了主从模式在解决适应度评价值计算量大的问题时,可达到接近线性的加速比。     

一种基于预测计算的无回溯智能组卷策略

针对中小型题库组卷成功率差且执行效率低的问题,提出了一种基于预测计算的无回溯智能组卷算法.该算法通过预测计算的方法,根据约束条件在访问数据库之前先计算出满足题库题量要求的试卷章节题型分配矩阵,提高组卷成功率,使得数据库访问时无回溯,并在具体选题时按照策略采用批量抽取的方案提高组卷速度.算法具有一定的通用性并为构建智能考试系统提供较高的研究参考价值.     

基于改进遗传算法的自动组卷问题研究

研究自动组卷优化问题,由于题库组卷的随机性,难度很大.在自动组卷研究中,遗传算法(GA)容易出现早熟、收敛速度慢等问题,为了快速可信地组卷,提出一种基于改进遗传算法的智能组卷算法(IGA).IGA算法在传统遗传算法的基础上,用符合组卷问题特点的实数编码、条件初始种群和分段交叉和变异算子来保证种群的多样性,防止早熟现象,采用加权误差的适应度函数加快收敛速度.通过进行仿真,结果表明,IGA相对于自适应遗传算法和标准遗传算法,提高了组卷有效性、稳定性和计算效率,能有效解决自动组卷问题.     

组合优化问题中GA性能分析及多样性评价

随着生产调度、机器学习、最优规划等组合优化问题的大规模化，复杂化，传统的基于运筹学的搜索算法已显得无能为力．具有广域搜索能力的遗传算法(GA)也因“完备性”与“健全性”的不充分不能有效地对应上述问题．为此，本文提出了保证GA上述两个性质地方法，使其能有效地解决复杂组合优化问题．     

一种函数优化问题的混合遗传算法

将传统的局部搜索算法和遗传算法相结合,可以较好地解决遗传算法在达到全局最优解前收敛慢的问题.文章给出一种结合可变多面体法和正交遗传算法的混合算法.实验表明,它通过对问题的解空间交替进行全局和局部搜索,能更有效地求解函数优化问题.     

基于改进遗传算法的自动组卷问题的研究

自动组卷优化问题的研究,由于题库组卷的随机性,难度很大。针对传统遗传算法在自动组卷中存在的未成熟收敛和收敛速度慢等问题,为了快速可信地组卷,提出了一种改进的遗传算法。该算法采用模拟小生境法选择算子进行种群选取,并对交叉算子和变异算子进行了优化,实现了交叉和变异概率的非线性自适应调整。进行仿真实验,结果证明,改进的遗传算法在组卷的有效性、稳定性和收敛速度等方面有显著的提高,更能有效解决自动组卷问题,具有较好的使用性能和实用性,能够极大的满足用户组卷的需求。     

基于改进遗传算法的多脉冲交会轨道优化研究

最优空间多脉冲轨道交会是一个复杂的非线性系统。由于结构复杂,存在非线性特性,影响系统的快速性和实时性。建立最优空间多脉冲轨道交会模型,传统遗传算法不能满足要求,因此提出了在自适应遗传算法的基础上引入多位变异的多变异位自适应遗传算法,对空间多脉冲交会轨道优化进行了求解。多变异位自适应遗传算法增加了种群的多样性,可避免算法的早熟收敛现象。仿真结果表明,利用多位变异自适应遗传算法求解空间多脉冲交会轨道优化效果好,避免了早期收敛,提高了全局寻优能力,为多脉冲交会轨道优化提供了较好的方法。     

基于改进遗传算法的车牌图像分割

为了解决简单遗传算法存在的早熟、收敛到全局最优解难问题,提出了适应度标定公式,提高选择压力;定义相似度概念,保留相似性差的个体,剔除相似性个体.在不增加群体规模的前提下,增加了群体的多样性.改进遗传算法能够避免简单遗传算法所存在的问题.为了解决车牌图像识别率低问题,提出基于改进遗传算法的车牌图像分割方法,进行车牌图像识别.实验证明利用此方法进行车牌图像识别效果较好.     

Genetic algorithms in computer aided design

Design is a complex engineering activity, in which computers are more and more involved. The design task can often be seen as an optimization problem in which the parameters or the structure describing the best quality design are sought.Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way.Genetic algorithms transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the ‘survival of the fittest’ principle. We present here the main features of genetic algorithms and several ways in which they can solve difficult design problems. We briefly introduce the basic notions of genetic algorithms, namely, representation, genetic operators, fitness evaluation, and selection. We discuss several advanced genetic algorithms that have proved to be efficient in solving difficult design problems. We then give an overview of applications of genetic algorithms to different domains of engineering design.     

免疫组播路由选择算法

研究了带宽延时受限、费用最小的QoS组播路由问题，并提出了一种解决该问题的免疫算法．免疫算法的核心在于免疫算子的构造，而它又是通过接种疫苗和免疫选择两个步骤来完成的．根据QoS组播路由问题，给出了免疫疫苗选取与免疫算子构造的具体方法．将免疫算法应用于组播路由选择，是通过在基于遗传算法的组播路由选择的基础上引入免疫算子来实现的．该算法采用的进化算子简便、高效．仿真实验表明，该算法不仅有效可行，而且较好地解决了标准遗传算法中出现的退化现象，提高了收效速度和搜索能力．     

Genetic algorithms with age structure

 This paper deals with genetic algorithms with age structure. Evolutionary optimization methods have been successfully applied to complex optimization problems, but the evolutionary optimization methods have a problem of bias in candidate solutions due to genetic drift in search. To solve this problem, we propose the introduction of age structure into genetic algorithms as a simple extension. In nature, an individual is removed from a population when the individual reaches lethal age. Therefore, genetic algorithms with age structure (ASGA) can maintain the genetic diversity of a population by removing aged individuals from the population. First, we conduct simple simulations of two subpopulations considering the age structure. Next, we apply the ASGA to a kanapsack problem. Finally, we discuss the optimal parameters for the age structure of the ASGA. These simulation results indicate that the ASGA can control selection pressure by aging process and relatively maintain the genetic diversity of a population. Received: 17 February 1997/Accepted: 6 May 1997     

A comparison of three artificial life techniques for reporting cell planning in mobile computing

Location management is a very important and complex problem in today's mobile computing environments. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper compares several well-known artificial life techniques to gauge their suitability for solving location management problems. Due to their popularity and robustness, a genetic algorithm (GA), tabu search (TS), and ant colony algorithm (ACA) are used to solve the reporting cells planning problem. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such a planner, a GA, TS, as well as several different AC algorithms are implemented. The effectiveness of each algorithm is shown for a number of test problems.     

Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs

We study a general equilibrium system where agents have heterogeneous beliefs concerning realizations of possible outcomes. The actual outcomes feed back into beliefs thus creating a complicated nonlinear system. Beliefs are updated via a genetic algorithm learning process which we interpret as representing communication among agents in the economy. We are able to illustrate a simple principle: genetic algorithms can be implemented so that they represent pure learning effects (i.e., beliefs updating based on realizations of endogenous variables in an environment with heterogeneous beliefs). Agents optimally solve their maximization problem at each date given their beliefs at each date. We report the results of a set of computational experiments in which we find that our population of artificial adaptive agents is usually able to coordinate their beliefs so as to achieve the Pareto superior rational expectations equilibrium of the model.     

一种求解MSA问题的自适应遗传算法

多序列比对(MSA)在生物信息学研究中占有重要地位，MSA问题是一个典型的NP问题，遗传算法是求解NP完全问题的一种有效方法。文章针对MSA问题，提出了一种新型自适应遗传算法，根据群体的多样性自适应调节变异概率，有效消除了算法中的欺骗性条件，使用突变算子来确保算法的搜索能力。整个算法模拟了自然界进化的周期性，较好的解决了群体的多样性和收敛深度的矛盾。算法的分析和测试表明，该算法是有效的。     

遗传算法用于结晶过程动力学参数辩识

遗传算法是一类随机优化方法。常被用于解决复杂的优化问题,基于群体的搜索,重组和变异是遗传算法区别于其他优化方法的主要特征。文章中将遗传算法应用于过饱和溶液Ｌｉ２Ｏ．３Ｂ２Ｏ３－Ｈ２Ｏ体系结晶过程动力学参数辨识,确定了结晶反应速率常数、热力学平衡浓度和表观反应级数。     

基于关键链遗传操作的机器人路径规划

针对复杂环境下遗传算法规划路径难的问题,提出一种基于关键链遗传操作的机器人路径规划方法。将回退策略和禁忌策略与启发式邻域搜索相结合保证路径的可行性。通过提取初始可行路径中的关键链,降低算法所需存储空间及计算代价。对关键链进行局部自适应变异和交叉操作,增强算法的优化能力。实验结果表明,该方法能有效地规划复杂环境下的机器人运动路径,算法性能优于同类算法,规划时间可满足实际应用需求。