高梁分子遗传图谱研究进展

本文从临时性作图群体和永久性作图群体两方面概述了高粱分子标记遗传图谱的研究进展,比较了不同分子标记和不同作图群体在高粱图谱构建上的差异,展望了高粱遗传图谱的研究方向.     

Assessing the counselling needs of high school students in Kenya

The Student Counselling Needs Scale (SCNS) was administered to 867 participants recruited from high schools in Kenya. The data were analyzed using exploratory factor analysis yielding five factors: human relationships, career development, self development, social values, and learning skills were assessed. The findings highlighted the importance of using assessment instruments to identify students’ counselling needs in Kenya.     

快速、准确的图像阈值分割新方法

图像分割是数字图像处理系统中基本而关键的技术.通过阈值分割是其最简单的技术,它假设目标和背景是可以分离的.详细讨论将自适应遗传算法与OTSU算法相结合应用于最佳阈值的确定中,提出了相应的算法并用于图像分割,由于自适应遗传算法具有全局搜索最优解能力,因此能在很短时间内自动确定阈值.实验仿真结果表明,该方法不仅可以实现准确的图像分割,并且使得分割速度大大提高.     

基于改进小生境遗传算法的自适应组卷系统

针对传统基于简单遗传算法的组卷系统收敛速度慢,组卷质量较差等缺陷,设计一种基于改进小生境遗传算法的自适应组卷系统。首先依照组卷约束权重比,生成适应度值较高的初始种群．其次对选择策略进行优化。引入小生境预选机制以维持种群多样性,防止过早收敛。再次根据组卷特点改进交叉算子和变异算子,根据种群进化情况,自动调整交叉概率和变异概率。最后经过若干迭代后达到终止条件,以自适应方式生成目标试卷。实验结果证明,与简单遗传算法相比,改进的遗传算法在收敛速度和组卷质量上均有显著的提高。     

GLABROUS INFLORESCENCE STEMS regulates trichome branching by genetically interacting with SIM in Arabidopsis

Arabidopsis trichomes are large branched single cells that protrude from the epidermis. The first morphological indication of trichome development is an increase in nuclear content resulting from an initial cycle of endoreduplication. Our previous study has shown that the C2H2 zinc finger protein GLABROUS INFLORESCENCE STEMS (GIS) is required for trichome initiation in the inflorescence organ and for trichome branching in response to gibberellic acid signaling, although GIS gene does not play a direct role in regulating trichome cell division. Here, we describe a novel role of GIS, controlling trichome cell division indirectly by interacting genetically with a key endoreduplication regulator SIAMESE (SIM). Our molecular and genetic studies have shown that GIS might indireclty control cell division and trichome branching by acting downstream of SIM. A loss of function mutation of SIM signficantly reduced the expression of GIS. Futhermore, the overexpression of GIS rescued the trichome cluster cell phenotypes of sim mutant. The gain or loss of function of GIS had no significant effect on the expression of SIM. These results suggest that GIS may play an indirect role in regulating trichome cell division by genetically interacting with SIM.     

Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423–431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.     

生物制药产业技术创新能力系统演化的遗传算法模型与仿真研究

基于CAS理论视角,明确生物制药产业技术创新能力的定义,构建生物制药产业技术创新能力系统的概念模型图,找出组成该系统的产业技术创新流程及惯例,并阐述生物制药产业技术创新能力系统的演化机制。与此同时,构建生物制药产业技术创新能力系统演化的遗传算法模型,并通过Matlab软件仿真生物制药产业技术创新能力系统的演化。结果表明,产业技术扩散对生物制药产业技术创新能力强度有显著增强作用,而产业吸收能力作为技术创新惯例则在适应度函数的迭代计算中起到显著约束的作用。     

Nonlinear modelling of a SOFC stack by improved neural networks identification

The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.     

Using genetic algorithms to evolve a population of topical queries

Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve “good query terms” in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.