Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.     

Community OR — Five Years of Organised Activities and Beyond

What has become known as Community Operational Research has been around in the UK for many years. The election of Jonathan Rosenhead as President of the UK OR Society in 1986–87 was the catalyst for bringing together and expanding activities which had been carried out largely by individuals working in isolation on a voluntary basis. The following five years have seen the establishment of the Community OR Unit at Northern College, the Centre for Community OR at Hull University and the Community OR Network, which supports the activities of volunteer OR workers across the UK. This paper reviews the range and types of work which have been undertaken by various practitioners. An assessment of the success of community OR is made and a number of issues which have arisen are discussed. Finally, given the diverse nature of community OR, its practice and practitioners, there is some discussion about the future of the area.     

论社区服务产业化

社区经济作为一种优化的资源配置方式，利用社区服务为载体，将社区内互不相联的各种经济成份变为利益共同体，建立一种新的经济生产方式，从而带动社区乃至更广区域的经济发展。     

The effects of induction conditions on production of a soluble antitumor sFv in Escherichia coli

CC49 is a second generation monoclonal antibody(mAb) with highaffinity to a pancarcinoma antigen, TAG-72. A single-chain Fv(sFv)ofCC49 may have a role in managing human carcinomas. Most reportedsFvs have been expressed as insoluble products that must besolubilized and renatured. Soluble sFv expression is advantageousas activity can be assayed directly from the periplasmic fraction.Also, gene-level immunoconjugates may not be amenable to refoldingprotocols. Using a vector that carries the tac promoter andomp A signal, we have examined the effects of four variableson the expression and accumulation of soluble CC49 sFv: (i)linker sequence joining VL and VH, (ii) isopropylthio-ß-D-galactosideconcentration for induction, (iii) temprature, and (iv) theaddition of nonmetabolizable sugars to the medium. We have beenable to demonstrate, using rapidly prepared periplasmic extracts,that the yield of soluble sFv improves by the addition of 0.4Msucrose to the medium and by inducing expression with a verylow concentration of IPTG (0.02–0.03 mM). Under theseinduction conditions periplasmic extracts demonstrate increasedexpression of the sFv, as shown by the larger amount of a 27kDa band on SDS-polyacrylamide gel, and an increased abilityto inhibit binding of the mAb CC49 to immobilized tumor extracts.     

Evaluating Community Safety Programmes and Community Engagement: The Role of Qualitative Methods and Collaborative Approaches to Policy Research

This article explores the role of qualitative and collaborative methods when undertaking evaluation research into local community safety partnerships and neighbourhood strategies. It draws on recent examples of research and evaluation studies of regeneration and community safety programmes in a number of cities and towns in England. The discussion is set against the background of the major changes in policy relating to crime and community safety, particularly the introduction of Crime and Disorder Partnerships and their engagement with local communities. The value of qualitative methods in exploring some key questions is discussed, particularly within a collaborative approach to policy research.     

广义分布式天线系统上行分集性能分析

针对广泛存在于无线通信环境中的阴影效应对系统性能影响非常大的问题,采用与传统的微分集技术相比,能够有效地对抗这种阴影衰落的分布式天线技术来解决这个问题.为了获得该条件下系统的理论性能,分析了采用最大比合并和相干等增益合并分集的广义分布式天线系统.借助lognormal近似法,给出了其中断概率和误码率性能的高精度解析结果.分析表明,在存在阴影衰落的情况下,这种广义分布式天线系统能够同时获得较大的微/宏分集增益,非常适合未来的无线小区环境.     

Rapid detection of antigen binding by antibody fragments expressed in the periplasm of Escherichia coli

Bacterial expression systems can greatly facilitate proteinengineering of antibodies. We have developed a system for high-levelexpression of antibodies, antibody fragments, or hybrid antibodieswith novel effector functions in the periplasm of Escherichiacoli. From 5 ml of cells, a simple extraction yields sufficientmaterial for SDS-gel electro-phoresis, detection and characterizationof hapten binding. To demonstrate our system, heavy-chain variableregions and 1 light chains of a mouse anti-NP antibody weresynthesized as hybrid proteins with a bacterial signal peptide(Omp F). Each chain is secreted into the periplasm where processing(cleavage of the signal peptide), folding and heterodimer associationtake place. Periplasmic proteins are released by cold osmoticshock, and hapten-binding activity is easily detected withoutfurther manipulation. The ease of genetic engineering in thissystem will facilitate the production of immunoglobulin derivativesdesigned for specific applications, and expression of thesemolecules in a native state will allow the rapid screening ofcombinatorial libraries and the results of mutagenesis.     

基于迁移学习的卷积神经网络通道剪枝

卷积神经网络在计算机视觉等多个领域应用广泛,然而其模型参数量众多、计算开销庞大,导致许多边缘设备无法满足其存储与计算资源要求。针对其边缘部署困难,提出使用迁移学习策略改进基于BN层缩放因子通道剪枝方法的稀疏化过程。本文对比不同层级迁移方案对稀疏化效果与通道剪枝选取容限的影响;并基于网络结构搜索观点设计实验,探究其精度保持极限与迭代结构的收敛性。实验结果表明,对比原模型,采用迁移学习的通道剪枝算法,在精度损失不超过0.10的前提下,参数量减少89.1%,模型存储大小压缩89.3%;对比原剪枝方法,将剪枝阈值从0.85提升到0.97,进一步减少参数42.6%。实验证明,引入迁移策略更易实现充分的稀疏化,提高通道剪枝阈值选取容限,实现更高压缩率;并在迭代剪枝的网络结构搜索过程中,提供更高效的搜索起点,利于快速迭代趋近至搜索空间的一个网络结构局部最优解。     

民生视阈下提升高职院校社区服务能力的研究

服务社区是高职院校服务社会、实现自身发展的有效途径。通过对高职院校服务社区过程中存在的问题和原因分析,给出提升高职院校社区服务能力的对策及可行性建议,为今后的相关工作提供参考。     

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.