基于AQ覆盖框架的蚁群规则集学习算法

针对规则集学习问题,提出一种遵循典型AQ覆盖算法框架（AQ Covering Algorithm）的蚁群规则集学习算法（Ant-AQ）。在Ant-AQ算法中,AQ覆盖框架中的柱状搜索特化过程被蚁群搜索特化过程替代,从某种程度上减少了陷入局优的情况。在对照测试中,Ant-AQ算法分别和已有的经典规则集学习算法（CN2、AQ-15）以及R.S.Parpinelli等提出的另一种基于蚁群优化的规则学习算法 Ant-Miner在若干典型规则学习问题数据集上进行了比较。实验结果表明：首先,Ant-AQ算法在总体性能比较上要优于经典规则学习算法,其次,Ant-AQ算法在预测准确度这样关键的评价指标上优于Ant-Miner算法。     

FCV_1: A New Fast Greedy Covering Algorithm

In this paper,a new covering algorithm called FCV1 is presented.FCV1 comprises two algorithms,one of which is able to fast search for a partial rule and exclude the large portion of neggative examples,the other algorithm incorporates the more optimized greedy set-covering algorithm,and runs on a small portion of training examples.Hence,the training process of FCV1 is much faster than that of AQ15.     

覆盖表生成的禁忌搜索算法

组合测试可以有效检测待测系统中由参数间交互作用而引发的故障.在其30多年的发展过程中,覆盖表生成一直是关键问题之一,相关研究文献已达200多篇.作为一种有效的覆盖表生成算法,已有的禁忌搜索算法在所生成的覆盖表规模上具备一定的优势,但其解的质量和运算速度仍有提升空间;同时,这些算法实际应用能力较差,既不支持约束处理,也无法生成可变力度覆盖表.针对以上问题,提出了一种禁忌搜索算法.该算法从3个方面对已有的算法进行了改进：1）算法参数配置调优分pair-wise和爬山两阶段进行,确保使用较少配置条数最大程度击中最优配置,进一步提高算法生成覆盖表的规模;2）进行算法并行化,加速算法生成覆盖表的速度;3）增加约束处理和变力度处理,使算法可适应多种测试场景.实验结果表明,该算法在固定力度、变力度、带约束等多种类型覆盖表的规模上都具有一定优势,同时,并行化使算法平均加速2.6倍左右.     

Efficient Specialization of Relational Concepts

An algorithm is presented for a common induction problem, the specialization of overty general concepts. A concept is too general when it matches a negative example. The particular case addressed here assumes that concepts are represented as conjunctions of positive literals, that specialization is performed by conjoining literals to the overly general concept, and that the resulting specializations are to be as general as possible. Although the problem is NP-hard, there exists an algorithm, based on manipulation of bit vectors, that has provided good performance in practice.     

Multiple Translational Containment Part II: Exact Algorithms

We present exact algorithms for finding a solution to the two-dimensional translational containment problem: find translations for k polygons which place them inside a polygonal container without overlapping. The term kCN denotes the version in which the polygons are convex and the container is nonconvex, and the term kNN denotes the version in which the polygons and the container are nonconvex. The notation (r,k)CN, (r,k)NN, and so forth refers to the problem of finding all subsets of size k out of r objects that can be placed in a container. The polygons have up to m vertices, and the container has n vertices, where n is usually much larger than m. We present exact algorithms for the following: 2CN in time, (r,2)CN in time (for ), 3CN in time, kCN in or time, and kNN in time, where LP(a,b) is the time to solve a linear program with a variables and b constraints. All these results are improvements on previously known running times except for the last. The algorithm for kNN is slower asymptotically than the naive algorithm, but is expected to be much faster in practice. The algorithm for 2CN is based on the use of separating line orientations as a means of characterizing the solution. The solution to 3CN also uses a separating line orientation characterization leading to a simple and robust ``carrousel' algorithm. The kCN algorithm uses the idea of disassembling the layout to the left. Finally, the kNN algorithm uses the concept of subdivision trees and linear programming. Received July 11, 1994; revised August 22, 1995, and February 26, 1996.     

APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm.     

利用蚁群算法生成覆盖表:探索与挖掘

覆盖表生成问题是组合测试的重要研究内容之一,目前已有许多数学方法、贪心算法、搜索算法用于求解这一问题.蚁群算法作为一种能够有效求解组合优化问题的演化搜索算法,已被应用到求解覆盖表生成问题中.已有的研究工作表明:蚁群算法适于求解一般覆盖表、变力度覆盖表生成以及覆盖表排序等问题,但算法结果与其他覆盖表生成方法相比并不具有优势.为了进一步探索与挖掘蚁群算法生成覆盖表的潜力,进行了如下4个层次的改进工作:(1)算法变种集成;(2)算法参数配置优化;(3)演化对象结构调整及演化策略改进;(4)利用并行计算优化算法时间开销.实验结果表明:通过以上4个层次的改进,蚁群算法生成覆盖表的性能有了显著提升.     

一种带约束条件的关联规则频繁集挖掘

论文先提出顺序单调约束和反顺序单调约束的概念并对其所包含的数学性质进行了讨论,在此基础上将其运用于频繁集挖掘过程中,给出挖掘基于顺序反单调性约束的频繁集算法和挖掘基于顺序单调约束的频繁集算法。带约束条件的关联规则频繁集挖掘可减少生成无意义的规则;同时,在频繁集生成过程,利用约束条件对搜索空间进行修剪,可提高挖掘算法的效率。     

求解带容量和时间窗约束车辆路径问题的改进蝙蝠算法

带时间窗和容量约束的车辆路径问题是车辆路径问题重要的扩展之一,属于NP难题,精确算法的求解效率较低,且对于较大规模问题难以在有限时间内给出最优解.为了满足企业和客户快速有效的配送需求,使用智能优化算法可以在有限的时间内给出相对较优解.研究了求解带容量和时间窗约束车辆路径问题的改进离散蝙蝠算法,为增加扰动机制,提高搜索速度和精度,在对客户点按其所在位置进行聚类的基础上,在算法中引入了变步长搜索策略和两元素优化方法进行局部搜索.仿真实验结果表明,所设计算法具有较高寻优能力和较强的实用价值.     

一种高效的FDE并行传播算法

约束规划(constraint programming, CP)是表示和求解组合问题的经典范式之一.扩展约束(extensional constraint)或称表约束(table constraint)是约束规划中最为常见的约束类型.绝大多数约束规划问题都可以用表约束表达.在问题求解时,相容性算法用于缩减搜索空间.目前,最为高效的表约束相容性算法是简单表约缩减(simple table reduction, STR)算法簇,如Compact-Table (CT)和STRbit算法.它们在搜索过程中维持广义弧相容(generalized arc consistency, GAC).此外,完全成对相容性(full pairwise consistency, fPWC)是一种比GAC剪枝能力更强的相容性.最为高效的维持fPWC算法是PW-CT算法.多年来,人们提出了多种表约束相容性算法来提高剪枝能力和执行效率.因子分解编码(factor-decomposition encoding, FDE)通过对平凡问题重新编码.它一定程度地扩大了问题模型,使在新的问题上维持相对较弱的GAC等价于在原问题...     

A deterministic (2−2/(k+1))n algorithm for k-SAT based on local search

Local search is widely used for solving the propositional satisfiability problem. Papadimitriou (1991) showed that randomized local search solves 2-SAT in polynomial time. Recently, Schöning (1999) proved that a close algorithm for k-SAT takes time (2−2/k)ⁿ up to a polynomial factor. This is the best known worst-case upper bound for randomized 3-SAT algorithms (cf. also recent preprint by Schuler et al.).We describe a deterministic local search algorithm for k-SAT running in time (2−2/(k+1))ⁿ up to a polynomial factor. The key point of our algorithm is the use of covering codes instead of random choice of initial assignments. Compared to other “weakly exponential” algorithms, our algorithm is technically quite simple. We also describe an improved version of local search. For 3-SAT the improved algorithm runs in time 1.481ⁿ up to a polynomial factor. Our bounds are better than all previous bounds for deterministic k-SAT algorithms.     

一种基于时间戳的简单表缩减算法

表约束是一种外延的知识表示方法,每个约束在对应的变量集上列举出所有支持或禁止的元组.广义弧相容（generalized arc consistency,简称GAC）是求解约束满足问题应用最广泛的相容性.Simple Tabular Reduction（STR）是一类高效的维持GAC的算法.在回溯搜索中,STR动态地删除无效元组,降低了查找支持的开销,并拥有单位时间的回溯代价,在高元表约束上获得了广泛运用,并有大量基于STR的改进算法被提出,其中,元组集的压缩表示是目前研究较多的方法.同样基于动态维持元组集有效部分的思想,为STR提出一种检测并删除无效元组和为变量更新支持的算法,作用于原始表约束并拥有单位时间的回溯代价.实验结果表明,该算法在表约束上维持GAC的效率普遍高于现有的非基于压缩表示的STR算法,并且在一些实例上的效率高于最新的基于元组集压缩表示的STR算法.     

基于覆盖的构造性学习算法SLA及在股票预测中的应用

覆盖算法是神经网络学习算法中的一个十分有效的方法，它克服了基于搜索机制的学习方法和规划学习方法计算复杂性高，难以用于处理海量数据的不足，为神经网络提供一个构造性的学习方法，但该方法是建立在所有训练样本都是精确的假设上的，未考虑到所讨论的数据具有不精确的情况，若直接将该方法应用于数据不精确情况，所得到效果不理想．主要讨论数据具有不精确情况下的时间序列的预测问题，为此将原有的覆盖算法进行改进，引入“覆盖强度”和“拒识样本”的概念，并结合这些新概念给出相应的覆盖学习算法(简称SLA)，最后将SLA算法，应用于金融股市的预测，具体应用到以上(海)证(券)综合指数构成的时间序列的预测，取得了较好的结果，这表明了SLA方法的可行性和应用前景。     

Search based constrained test case selection using execution effort

Software testing is essential to guarantee high quality products. However, it is a very expensive activity, particularly when manually performed. One way to cut down costs is by reducing the input test suites, which are usually large in order to fully satisfy the test goals. Yet, since large test suites usually contain redundancies (i.e., two or more test cases (TC) covering the same requirement/piece of code), it is possible to reduce them in order to respect time/people constraints without severely compromising coverage. In this light, we formulated the TC selection problem as a constrained search based optimization task, using requirements coverage as the fitness function to be maximized (quality of the resultant suite), and the execution effort (time) of the selected TCs as a constraint in the search process. Our work is based on the Particle Swarm Optimization (PSO) algorithm, which is simple and efficient when compared to other widespread search techniques. Despite that, besides our previous works, we did not find any other proposals using PSO for TC selection, neither we found solutions treating this task as a constrained optimization problem. We implemented a Binary Constrained PSO (BCPSO) for functional TC selection, and two hybrid algorithms integrating BCPSO with local search mechanisms, in order to refine the solutions provided by BCPSO. These algorithms were evaluated using two different real-world test suites of functional TCs related to the mobile devices domain. In the performed experiments, the BCPSO obtained promising results for the optimization tasks considered. Also, the hybrid algorithms obtained statistically better results than the individual search techniques.     

覆盖表生成的遗传算法配置参数优化

覆盖表生成是组合测试的关键问题,很多数学方法、贪心算法以及演化搜索方法等被应用于生成各种覆盖表.针对演化搜索方法的性能受到方法本身配置参数影响很大这一实际问题,文中以二维覆盖表生成为实例,系统地对典型的演化搜索方法——遗传算法的种群规模、进化代数、交叉概率、变异概率以及遗传算法的变种算法等因素进行探索,设计了pair-wise法、Base choice法和爬山法3条实验路线探索遗传算法的这些配置参数及其相互作用对算法生成二维覆盖表效果的影响,并回答两个问题:对于特定二维覆盖表生成问题,是否存在遗传算法的最优参数配置;对于一般的二维覆盖表生成问题,是否存在通用的遗传算法最优参数配置.     

Real-time navigation using randomized kinodynamic planning with arrival time field

In this paper, we propose a novel path planning algorithm for a mobile robot in dynamic and cluttered environments with kinodynamic constraints. We compute the arrival time field as a bias which gives larger weights for shorter and safer paths toward a goal. We then implement a randomized path search guided by the arrival time field for building the path considering kinematic and dynamic (kinodynamic) constraints of an actual robot. We also consider path quality by adding heuristic constraints on the randomized path search, such as reducing unstable movements of the robot by using a heading criterion. The path will be extracted by backtracking the nodes which reach the goal area to the root of the tree generated by the randomized search, and the motion from the very first node will be sent to the robot controller. We provide a brief comparison between our algorithm and other existing algorithms. Simulation and experimental results prove that our algorithm is fast and reliable to be implemented on the real robot and is able to handle kinodynamic problems effectively.