An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Sperm metabolism is altered during storage by female insects: evidence from two-photon autofluorescence lifetime measurements in bedbugs

We explore the possibility of characterizing sperm cells without the need to stain them using spectral and fluorescence lifetime analyses after multi-photon excitation in an insect model. The autofluorescence emission spectrum of sperm of the common bedbug, Cimex lectularius, was consistent with the presence of flavins and NAD(P)H. The mean fluorescence lifetimes showed smaller variation in sperm extracted from the male (tau m, τ_m = 1.54–1.84 ns) than in that extracted from the female sperm storage organ (tau m, τ_m = 1.26–2.00 ns). The fluorescence lifetime histograms revealed four peaks. These peaks (0.18, 0.92, 2.50 and 3.80 ns) suggest the presence of NAD(P)H and flavins and show that sperm metabolism can be characterized using fluorescence lifetime imaging. The difference in fluorescence lifetime variation between the sexes is consistent with the notion that female animals alter the metabolism of sperm cells during storage. It is not consistent, however, with the idea that sperm metabolism represents a sexually selected character that provides females with information about the male genotype.     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

GOS+MU:一种查询对象选择新方法

在分析单一MU(Most Uncertainty)采样缺陷的基础上,提出一种"全局最优搜寻"方法 GOS(Global Optimal Search),并结合MU共同完成查询选择。GOS+MU方法中,GOS着眼全局寻找目标,在应用环境能提供的训练样本数量有限、分类器受训不充分时,该方法选择的对象学习价值高,能快速推进分类器学习进程;MU则能够在GOS采样失效情形下,利用分类器当前训练成果,选择查询不确定性最强的样本补充训练集。通过对网络商品的用户评论进行分类仿真,并比较其他采样学习方法的效果,证明了GOS+MU方法在压缩学习成本、提高训练效率方面的有效性。     

Supramolecular Glyco‐nanoparticles Toward Immunological Applications

Glyco‐mimicking nanoparticles (glyco‐NPs) with Förster resonance energy transfer (FRET) donor and acceptor groups formed via dynamic covalent bond of benzoboroxole and sugar from two complementary polymers are prepared. The glyco‐NPs are proved to be quite stable under physiological conditions but sensitive to pH. So the glyco‐NPs can be internalized by dendritic cells with integrity and nontoxicity and then dissociate within the acidic organelles. This particle dissociation is directly observed and visualized in vitro, for the first time via the FRET measurements and fluorescent microscopy. This feature makes controlled release of drug or protein by glyco‐NPs possible, i.e., when model antigen Ovalbumin is loaded in the glyco‐NPs, the released Ovalbumin in dendritic cells stimulates T cells more efficiently than the free Ovalbumin itself as a result of the enhanced antigen processing and presentation. Thus, the results enlighten a bright future of the glyco‐NPs in immunotherapy.     

宁夏宁东供水工程主机设备形式参数选择与国际招标

宁东供水工程供水水泵共有卧式和立式离心泵2种方案可供选择,也是工程论证时争论的焦点,经综合比较及主机设备的国际招标,最终确定了奥地利安德里兹公司的卧式双吸中开式离心泵为宁东供水工程的供水水泵。     

浅析高校新校区选址主要影响因素

从政策、城市与区域规划、环境、文化及其传承因素、基础设施等方面探析了高校新校区选址有关的主要影响因素，以满足高等学校大规模的扩建要求，促进高等教育的发展。     

论建立动态勘探目标评价体系的迫切必要性

从国际上通行的油气勘探评价工作重点,中国石油所面临的"已逐步进入一个不断在老区范围内开展精细勘探、在老区范围内开展新区块、新层系勘探以获得主要的储量增长"的新的勘探形势,现有地质研究中各专题研究与具体勘探目标评价脱节等多个方面,论述了在中国石油内部建立一套动态勘探目标评价体系的迫切必要性;并在此基础上阐述了一个完整的动态勘探目标评价体系应包括的主要内容;最后对该体系的建设进行了可行性分析。     

DSR的一种自适应路由自动缩短机制

动态源路由协议是无线自组织网络众多路由协议中被广泛关注的一种按需路由协议，目前，关于该协议的优化措施有很多。本文先分析了动态源路由协议的一种优化机制——路由自动缩短，该机制能动态缩短处于工作状态路由的跳数，但不保证缩短路由的质量，在此基础上，提出一种自适应路由自动缩短机制，新机制既可缩短路由．又能保证缩短路由质量．理论分析及仿真结果表明，自适应路由缩短机制的各项性能优于原路由缩短机制。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.