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.     

试题库随机组卷系统的研究与探讨

组卷和试卷测评是网络考试系统的核心内容,为此,在深入研究的基础上,提出了基于难度级别的多约束组题算法,实现了快速组卷过程。基于该组卷方式,介绍了试卷的3个主要评测指标。该算法已经成功应用于实际网络考试系统中。     

VB程序实现考试系统自动抽题

组卷即从已有的题库中从众多的试题中,按考试要求选出若干道题来组成一份高质量的试卷。考试成功与否,组卷是关键,在此提出了一种高效便捷的组卷算法并用VB语言将其实现。     

试题库系统中随机抽题算法的设计与实现

针时职业教育精品网站的特点,提出适应于网络教育的以知识点为核心的自动组卷算法.该算法把题型难度比例、题型个数作为主要控制目标,从而按知识点选题组卷,并运用适当的随机算法组卷策略优化组卷结果.实验表明,该算法依据不同的组卷策略自动生成的试卷,组卷效率、成功率和知识点覆盖率均比较理想.     

网络考试题库自动组卷策略研究

组卷是网络考试系统的核心部分,通过组卷来决定试卷的题量、试卷的知识点分布、试卷类型及考试时间的多少。组卷系统是实现考试规范化、公平化、合理化的重要途径。因此,为了解决在网络教学平台中开发和提供功能完善的网络组卷系统这一问题,对网络考试题库自动组卷策略进行了探讨。     

基于Delphi程序设计在考试管理中的作用探讨

文章结合Delphi程序设计在考试管理的作用,叙述了试卷生成系统设计思路及结构,接着给出数据库逻辑结构与试卷生成流程。试卷生成系统能根据试题难易度及题型题数自动组卷或手工组卷、能浏览增删或修改题库中的题目、能对生成的试卷保存并打印。     

基于遗传算法的成卷策略的设计与实现

本文提出了一个运用遗传算法来进行成卷的策略。它根据成卷要求，运用统计学的知识来进行以概率选题；并且保留了适值比较大的染色体到下一代，这样防止了早熟现象的发生。最后得出的试卷不仅能满足命题要求，还杜绝了在一张试卷中出现重题。     

基于知识点和改进随机抽取算法的智能组卷方案研究

组卷和试卷测评是在线测试系统的核心内容。文章提出了基于知识点和改进随机抽题算法的智能组卷方案,实现了快速组卷过程。该算法已经成功应用于实际在线测试系统中。     

技能点组合分布的技能操作题库组卷研究①

根据技能组卷特点提出围绕技能点构建组卷约束模型,使用组合分布策略实现技能题库自动组卷。设计了技能点组卷矩阵约束模型,重点探讨组合分布组卷策略实现的三个关键环节：参数验证优化、技能点组合方案生成和技能试卷生成。通过实例验证表明：使用该方法实现技能题库组卷完全满足技能,点组卷矩阵约束条件,具有组卷方法简便、组卷效率高、试卷质量优的特点,非常适合技能题库的组卷应用。     

技能点组合分布的技能操作题库组卷研究①

根据技能组卷特点提出围绕技能点构建组卷约束模型,使用组合分布策略实现技能题库自动组卷。设计了技能点组卷矩阵约束模型,重点探讨组合分布组卷策略实现的三个关键环节：参数验证优化、技能点组合方案生成和技能试卷生成。通过实例验证表明：使用该方法实现技能题库组卷完全满足技能点组卷矩阵约束条件,具有组卷方法简便、组卷效率高、试卷质量优的特点,非常适合技能题库的组卷应用。     

A sequential approach for unconstrained optimization via a partitioning technique

In this paper, an alternative optimization strategy incorporating the ideas of lexicographic optimization and evolutionary algorithms is presented. The given optimization problem is approximated by others in which priorities are given. Under the sequential optimization method, they are optimized, not exhaustively, in order to produce an initial point for the given problem. An important role in the proposed approach plays the way of generating the involved problems and the given priorities on them. General principles to produce the objective functions of the involved problems are proposed. An algorithm named LexOpt Algorithm, which implements the suggested process, is given. Numerical results via LexOpt Algorithm, on a set of widely used test problems show noticeable promising convergence behaviour of the proposed strategy in comparison with the utilized optimization methods.     

Test problem generator for unconstrained global optimization

We develop in this paper a high performance test problem generator for generating analytic and highly multimodal test problems for benchmarking unconstrained global optimization algorithms. More specifically, we propose in this research a novel and computationally efficient procedure for generating nonlinear nonconvex not separable unconstrained test problems with (i) analytic test functions, (ii) known local minimizers that are distributed uniformly in the interior of a compact box, among which only one is the global solution, and (iii) controllable difficulty levels. A standard set of test problems with different sizes and different difficulty levels is produced for both MATLAB and GAMS and is available for downloading. Numerical experiments have demonstrated the stability of the generating process and the difficulty of solving the standard test problems.     

Memetic算法在智能组卷问题中的应用

Memetic算法是一种启发式搜索方法,常用于解决一些NP问题。本文通过对遗传Memetic算法的改进与优化,结合智能组卷问题的特点,提出一套完整的解决方案。算法使用Memetic算法框架,全局搜索策略采用分段实数编码的遗传算法,融合了算法的交叉变异操作,局部搜索策略采用模拟退火算法,有效解决陷入局部最优问题。通过不同算法的对比实验表明,本文提出的Memetic算法能够快速高效地解决智能组卷问题,大大提升试卷生成质量,减少迭代次数,可快速获得最优解。      

多约束分级寻优结合预测计算的智能组卷策略

针对智能组卷中多约束制约降低组卷成功率且难以实现知识点自动均匀分布的问题,提出一种多约束分级寻优的策略,通过分级降低问题规模,利用树形结构管理知识点实现知识点均匀分布;针对中小型题库组卷成功率低的问题,在分级寻优中针对章节约束和题型约束提出了一种基于预测计算的无回溯的智能组卷算法,提高组卷成功率。实验测试表明,算法适用于大、中、小型题库,均能得到较理想的组卷结果。     

Facilities layout design by genetic algorithms

Genetic algorithms (GAs) are a class of adaptive search techniques which have gained popularity in optimisation. In particular they have successfully been applied to NP hard problems such as those resulted in mathematical modelling of facilities design problems. The typical steps required to implement GAs are: encoding of feasible solutions into chromosomes using a representation method, evaluation of fitness function, setting of GAs parameters, selection strategy, genetic operators, and criteria to terminate the process. This paper reports on finding of a research in design of a GA solving the quadratic assignment formulation of equal and unequal-sized facilities layout problems. Comparison is made with solutions of several test problems reported in the literature.     

A new modification approach on bat algorithm for solving optimization problems

Optimization can be defined as an effort of generating solutions to a problem under bounded circumstances. Optimization methods have arisen from a desire to utilize existing resources in the best possible way. An important class of optimization methods is heuristic algorithms. Heuristic algorithms have generally been proposed by inspiration from the nature. For instance, Particle Swarm Optimization has been inspired by social behavior patterns of fish schooling or bird flocking. Bat algorithm is a heuristic algorithm proposed by Yang in 2010 and has been inspired by a property, named as echolocation, which guides the bats’ movements during their flight and hunting even in complete darkness. In this work, local and global search characteristics of bat algorithm have been enhanced through three different methods. To validate the performance of the Enhanced Bat Algorithm (EBA), standard test functions and constrained real-world problems have been employed. The results obtained by these test sets have proven EBA superior to the standard one. Furthermore, the method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.     

A hyper-heuristic based framework for dynamic optimization problems

Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm.     

现场条件约束下的动态诊断策略研究

针对传统的故障诊断策略属于静态的诊断方法,难以在复杂的现场工作环境中应用的问题,在基于准深度优先搜索（QDFS）算法的基础上,提出了一种基于改进的QDFS算法（考虑测试的不确定性）的现场约束条件下的诊断策略生成方法。首先分析工作现场可能存在的条件约束,然后给出条件约束的描述方法,再根据改进的QDFS算法给出生成诊断策略的具体过程,最后用案例对方法进行验证。案例验证结果表明,该方法不仅考虑了不可靠测试的影响,并能在现场约束条件下应用起来,比传统的静态诊断策略更具有适应性和实用性。     

基于动态调整简化粒子群优化的组合测试用例生成方法

优化的组合测试中的一个关键是生成的测试用例能够覆盖更多的组合,而粒子群算法在生成强组合覆盖用例方面有其独特的优势和能力。文中提出了一种基于动态调整简化粒子群优化的组合测试用例生成方法。该方法基于粒子群算法生成测试用例,结合混合的优先级one-test-at-a-time策略和基于动态调整的简化粒子群算法生成组合测试用例集,排除了速度因素对粒子优化过程的影响。定义了一个粒子收敛指标,以粒子群早熟收敛程度为依据来动态调整惯性权值,以防止粒子陷入局部最优和后期出现收敛速度慢的情况,从而提高粒子群算法所生成的覆盖表的覆盖组合能力。通过对比实验表明,基于动态调整的简化粒子群优化算法在用例规模和时间成本上具有一定的优势。     

A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems

In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.