Range Assignment for Biconnectivity and k-Edge Connectivity in Wireless Ad Hoc Networks

Depending on whether bidirectional links or unidirectional links are used for communications, the network topology under a given range assignment is either an undirected graph referred to as the bidirectional topology, or a directed graph referred to as the unidirectional topology. The Min-Power Bidirectional (resp., Unidirectional) k-Node Connectivity problem seeks a range assignment of minimum total power subject to the constraint that the produced bidirectional (resp. unidirectional) topology is k-vertex connected. Similarly, the Min-Power Bidirectional (resp., Unidirectional) k-Edge Connectivity problem seeks a range assignment of minimum total power subject to the constraint the produced bidirectional (resp., unidirectional) topology is k-edge connected. The Min-Power Bidirectional Biconnectivity problem and the Min-Power Bidirectional Edge-Biconnectivity problem have been studied by Lloyd et al. [23]. They show that range assignment based the approximation algorithm of Khuller and Raghavachari [18], which we refer to as Algorithm KR, has an approximation ratio of at most 2(2 – 2/n)(2 + 1/n) for Min-Power Bidirectional Biconnectivity, and range assignment based on the approximation algorithm of Khuller and Vishkin [19], which we refer to as Algorithm KV, has an approximation ratio of at most 8(1 – 1/n) for Min-Power Bidirectional Edge-Biconnectivity. In this paper, we first establish the NP-hardness of Min-Power Bidirectional (Edge-) Biconnectivity. Then we show that Algorithm KR has an approximation ratio of at most 4 for both Min-Power Bidirectional Biconnectivity and Min-Power Unidirectional Biconnectivity, and Algorithm KV has an approximation ratio of at most 2k for both Min-Power Bidirectional k-Edge Connectivity and Min-Power Unidirectional k-Edge Connectivity. We also propose a new simple constant-approximation algorithm for both Min-Power Bidirectional Biconnectivity and Min-Power Unidirectional Biconnectivity. This new algorithm applies only to Euclidean instances, but is best suited for distributed implementation. A preliminary version of this work appeared in the proceedings of the 2nd International Conference on AD-HOC Network and Wireless (Adhoc-Now 2003). Research performed in part while visiting the Max-Plank-Institut fur Informatik. Gruia Calinescu is an Assistant Professor of Computer Science at the Illinois Institute of Technology since 2000. He held postdoc or visiting researcher positions at DIMACS, University of Waterloo, and Max-Plank Institut fur Informatik. Gruia has a Diploma from University of Bucharest and a Ph.D. from Georgia Insitute of Technology. His research interests are in the area of algorithms. Peng-Jun Wan has joined the Computer Science Department at Illinois Institute of Technology in 1997 and has been an Associate Professor since 2004. He received his Ph.D. in Computer Science from University of Minnesota in 1997, M.S. in Operations Research and Control Theory from Chinese Academy of Science in 1993, and B.S. in Applied Mathematics from Tsinghua University in 1990. His research interests include optical networks and wireless networks.     

基于遗传算法的服装工序编排规划

本文讨论了建立服装工序编排系统的必要性，在进行服装工序拆分、合并的基础上，应用遗传算法建立了以流水线平衡为目标的服装工序编排模型．     

心电地图仪中工频干扰的一种滤除方法

在心电地图仪中记录到的体表心电信号往往由于电磁的影响而引进工频干扰．基于体表心电信号中工频干扰的特点，作者提出了一种滤除工频干扰的方法，即就单独—胸导联信号通过提高频谱的分辨率来估计工频干扰的频率；基于最小均方误差准则来估计各导联信号中工频干扰的幅度和相位。文中还给出了该算法的流程图和滤波性能分析。模拟结果表明了该算法的有效性。应用该算法对心电地图仪中的工频干扰进行对消，取得了满意的结果。     

Evolutionary algorithms and de novo peptide design

One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the evolutionary algorithms implemented.     

国外农用非织造布覆盖材料的应用

非织造布在现代农业生产中占有重要地位 ,非织造布作为农用覆盖材料在柑橘、草莓、茶叶、花卉、水稻和玉米育苗以及人参栽培等多方面应用较为广泛 ,本文对国外农用非织造布覆盖材料的特点、性能以及应用情况作了介绍     

Specification methods for material-handling control algorithms in flexible manufacturing systems

Good methods are needed to specify, test, and debug material-handling control logic. This article surveys a number of representative methods for defining and describing control algorithms for programmable material-handling equipment used in flexible manufacturing systems. The methods are evaluated with regard to their suitability for communication between people and as bases for interfaces to automatic program generators. It is concluded that no single method is entirely satisfactory. Three methods (position diagrams, function block diagrams, and operation networks) have potential to be combined into an effective hybrid approach that minimizes the need for the user to switch between various conceptual models.     

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.     

A simple approximation algorithm for the weighted matching problem

We present a linear time approximation algorithm with a performance ratio of 1/2 for finding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [STACS'99, Lecture Notes in Comput. Sci., Vol. 1563, 1999, pp. 259-269]. Our algorithm uses a new approach which is much simpler than the one given by Preis and needs no amortized analysis for its running time.     

Response level crossing rate of a linear system excited by a partially specified Gaussian load process

The problem of determining the maximum mean response level crossing rate of a linear system driven by a partially specified Gaussian load process has been considered. The partial specification of the load is given only in terms of its total average energy. The critical input power spectral (PSD) function, which maximizes the mean response level crossing rate, is obtained. The critical input PSD turns out to be highly narrow-banded which fails to capture the erratic nature of the excitation. Consequently, the trade-off curve between the maximum mean response level crossing rate and the maximum disorder in the input process, quantified in terms of its entropy rate, has been generated. The method of Pareto optimization is used to tackle the conflicting objectives of the simultaneous maximization of the mean response level crossing rate and the input entropy rate. The non-linear multi-objective optimization has been carried out using a recently developed multi-criteria genetic algorithm scheme. Illustrative example of determining the critical input of an axially vibrating rod, excited by a partially specified stationary Gaussian load process, has been considered.     

A Search for Hidden Relationships: Data Mining with Genetic Algorithms

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.