Evolutionary graph drawing algorithms

In this paper, graph drawing algorithms based on genetic algorithms are designed for general undirected graphs and directed graphs. As being shown, graph drawing algorithms designed by genetic algorithms have the following advantages: the frames of the algorithms are unified, the method is simple, different algorithms may be attained by designing different objective functions, therefore enhance the reuse of the algorithms. Also, aesthetics or constrains may be added to satisfy different requirements. Foundation item: Supported by the National Natural Science Foundation of China(60133010,60073043,70071042) Biography: Huang Jing-wei ( 1956-), male, Professor, research direction: the design and analysis of algorithms and evolutionary computation.     

Drift analysis in studying the convergence and hitting times of evolutionary algorithms: An overview

This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with Markov chains or supermartingales. Then the drift conditions which guarantee the convergence of evolutionary algorithms are described. And next the drift conditions which are used to estimate the hitting times of evolutionary algorithms are presented. Finally an example is given to show how to analyse hitting times of EAs by drift analysis approach. Foundation item: Supported by Engineering and Physical Science Research Courcil (GR R52541 01) and State Laboratory of Software Engineering Foundation item: Supported by Engineering and Physical Science Research Courcil (GR R52541 01) and State Laboratory of Software Engineering Biography: He Jun( 1967-), male, visiting fellow at State Key Lab of Software Engineering, Ph D. , research direction: computational intelligence     

A Multi-objective optimal evolutionary algorithm based on tree-ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcomings, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time. Foundation item: Supported by the National Natural Science Foundation of China(60073043, 70071042, 60133010) Biography: Shi Chuan( 1978-), male, Master candidate, research direction; intellective computation, evolutionary computation.     

The evolutionary computation techniques for protein structure prediction: A survey

In this paper, the applications of evolutionary al gorithm in prediction of protein secondary structure and tertiary structures are introduced, and recent studies on solving protein structure prediction problems using evolutionary algorithms are reviewed, and the challenges and prospects of EAs applied to protein structure modeling are analyzed and discussed. Foundation item: Supported by the National Natural Science Foundation of China( 60133010,70071042,60073043) Biography: Zou Xiu-fen ( 1966-), female, Associate professor, research direction:evolutionary computing, parallel computing, bioinformatics.     

Applying evolutionary algorithm to public key cryptosystems

A best algorithm generated scheme is proposed in the paper by making use of the thought of evolutionary algorithm, which can generate dynamically the best algorithm of generating primes in RSA cryptography under different conditions. Taking into account the factors of time, space and security integrated, this scheme possessed strong practicability. The paper also proposed a model of multi-degree parallel evolutionary algorithm to evaluate synthetically the efficiency and security of the public key cryptography. The model contributes to designing public key cryptography system too. Foundation item: Supported by the Hi-Tech Research and Development Foundation item: Supported by the Hi-Tech Research and Development Biography; Tu Hang (1975-), male, Ph. D candidate, research directions: Biography; Tu Hang (1975-), male, Ph. D candidate, research directions:     

Point-tree structure genetic programming method for discontinuous function’s regression

A new point-tree data structure genetic programming (PTGP) method is proposed. For the discontinuous function regression problem, the proposed method is able to identify both the function structure and discontinuities points simultaneously. It is also easy to be used to solve the continuous function’s regression problems. The numerical experiment results demonstrate that the point-tree GP is an efficient alternative way to the complex function identification problems. Foundation item: Supported by the National Natural Science Foundation (60173046) and the Natural Science Foundation of Hubei Province (2002AB040) Biography: Xiong Sheng-wu (1966-), male, Associate professor, research direction: evolutionary computing, parallel computing.     

Analysis of negative correlation learning

This paper describes negative correlation learning for designing neural network ensembles. Negative correlation learning has been firstly analysed in terms of minimising mutual information on a regression task. By minimising the mutual information between variables extracted by two neural networks, they are forced to convey different information a-bout some features of their input. Based on the decision boundaries and correct response sets, negative correlation learning has been further studied on two pattern classification problems. The purpose of examining the decision boundaries and the correct response sets is not only to illustrate the learning behavior of negative correlation learning, but also to cast light on how to design more effective neural network ensembles. The experimental results showed the decision boundary of the trained neural network ensemble by correlation learning is almost as good as the optimum decision boundary. Foundation item: Supported by the National Natural Science Foundation of China (60133010) Biography: Liu Yong ( 1966-), male, Ph. D, Associate professor, research direction: evolutionary algorithms, neural networks, and evolvable hardware.     

Parallel implementations of modeling

First, an asynchronous distributed parallel evolutionary modeling algorithm (PEMA) for building the model of system of ordinary differential equations for dynamical systems is proposed in this paper. Then a series of parallel experiments have been conducted to systematically test the influence of some important parallel control parameters on the performance of the algorithm. A lot of experimental results are obtained and we make some analysis and explanations to them. Foundation item: Supported by the National Natural Science Foundation of China (60133010, 70071042, 60073043) Biography: Cao Hong-qing ( 1972-), female, Associate professor, research direction; evolutionary computing, parallel computing.     

Methods of constructing optimal wavelet filters based on genetic algorithm

In this paper, algorithms of constructing wavelet filters based on genetic algorithm are studied with emphasis on how to construct the optimal wavelet filters used to compress a given image,due to efficient coding of the chromosome and the fitness function, and due to the global optimization algorithm, this method turns out to be perfect for the compression of the images. Foundation item: Supported by the Natural Science Foundation of Education of Hunan Province(21010506) Biography: Wen Gao-jin( 1978-), male, Master candidate, research direction: evolutionary computing.     

A hybrid model for the mid-long term runoff forecasting by evolutionary computation techniques

The mid-long term hydrology forecasting is one of most challenging problems in hydrological studies. This paper proposes an efficient dynamical system prediction model using evolutionary computation techniques. The new model overcomes some disadvantages of conventional hydrology forecasting ones. The observed data is divided into two parts: the slow “smooth and steady” data, and the fast “coarse and fluctuation” data. Under thedivide and conquer strategy, the behavior of smooth data is modeled by ordinary differential equations based on evolutionary modeling, and that of the coarse data is modeled using gray correlative forecasting method. Our model is verified on the test data of the mid-long term hydrology forecast in the northeast region of China. The experimental results show that the model is superior to gray system prediction model (GSPM). Foundation item: Supported by the National Natural Science Foundation of China ( 60133010, 70071042, 60073043) Biography: Zou Xiu-fen(1966-),female,Associate professor, research direction:evolutionary computing, parallel computing.     

基于DNA进化算法的车辆调度问题

在建立一种物流配送车辆调度模型的基础上,针对传统启发式算法对复杂问题搜索效率低、易陷入局部最优的缺点,构造了DNA进化算法求解该问题。将其在个体变异操作中进行了一些改进,避免陷入局部最优。车辆调度问题(V SP)属于组合优化NP难题。采用V SP问题进行测试,仿真结果表明:该算法简单可行,与其他算法相比较,不但有很好的求解性能,而且具有更快的收敛能力与搜索能力。     

A niche-based evolutionary Tabu search algorithm for optimal coordination of protection relays in power networks

In this paper, refusal of operation and the degree of constraint violation are considered as one part of the objective function for optimizing the protection relay setting coordination of over current relays in power systems; a new expression of objective function in this optimization problem is proposed. To combine the advantages of both Tabu search (TS) and evolutionary algorithms (EAs), a new niche-based evolutionary Tabu search algorithm (named NETS) is presented to solve this optimization problem. Moreover, in two cases of power networks, comparisons between NETS and two published algorithms are given. Experimental results show the expression of the objective function for protection relay setting is feasible and reasonable, and the proposed algorithm NETS exhibits a good performance. Biography: YUAN Rongxiang (1965–), male, Professor, Ph. D., research direction: safety and stability in distributed power networks, relay protection, information and intelligent of power system.     

关于多目标演化算法的策略分析

基于Pareto排序算法的多目标演化算法是多目标演化算法所采用的重要方法,本文叙述了多目标演化算法(MOEAs)的有关概念,在分析已有算法的一些性能和特征的基础上,结合演化算法的有关概念,重点基于Pareto排序算法分析了影响多目标演化算法性能的两大方面:求解过程中解集合的多样性、均匀性分布的保持与维护以及解的收敛性,分析了MOEAs设计中需要注意的策略问题以及今后研究的重点.     

Automatic evolutionary modeling by the Lattice-Boltzmann method

The Lattice-Boltzmann method is an effective tool for solving fluid mechanics problems, but there isn’t still a good scheme to determinate some parameters in Boltzmann equations. In this paper, a technique using evolutionary algorithm to automatically model Boltzmann equations is introduced. Numerical simulation shows that the designed scheme is fast and efficient. Biography: Chen Ju-hua ( 1963-), male, Associate professor, research direction: Lattice-Boltzmann method, evolutionary algorithm.     

基于部分失真定理的信道最优矢量量化器设计

提出了一个使用进化算法，引入部分失真定理（渐进划分理论）的信道最优矢量量化器（COVQ）的设计算法，采用该算法，在给定信道状态模型和存在的信道噪声的情况下，可有效地提高矢量量化器的性能，实现了COVQ的设计，该算法利用进化策略调整各码矢所确定区域的子误差，从而进一步改善期望误差，与常用的码书设计算法相比较，该算法能较好地调整各区域的子误差，获得比传统算法更高的性能增益。     

Implementation of transaction level AMBA bus models using systemC

This paper deals with how to implement AMBA bus transaction level modeling in SystemC.There are twom ain techniques used in the whole modeling process,which consist of starting the platform modeling at the transaction level and using the uniformed modeling language-System C.According to the concepts of interface,port and hierarchical channel introduced in SystemC 2.0,the system of master-channel (AMBA bus) slave is created as the architecture of the AMBA bus transaction level model,which can make it more extendable.The port and interface classes of the model that are prone to program are defined in accordance with the SoC hierarchical design methodology,In addition,method calls,not signal communication,are used between different modules in the model,so the higher-level abstraction is achieved and the simulation performance is improved.The AMBA bus transaction level model is analyzed and certified by simulation experiment,and proved to be completely compliant to the AMBA specification 2.0.     

Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems

In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.     

Solving a kind of high complexity multi-objective problems by a fast algorithm

A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions. Foundation item: Supported by the National Natural Science Foundation of China (60204001, 70071042, 60073043, 60133010) and Youth Chengguang Project of Science and Technology of Wuhan City (20025001002). Biography: Zeng San-you ( 1963-), male, Associate professor, research direction: evolutionary computing, parallel computing     

Multi-Seed Key Distribution Scheme Test

The key problem of securing multieast is to generate, distribute and update Session Encryption Key（SEK）. Polynomial expansion with multi-seed （MPE） scheme is an approach which is based on Polynomial expansion （PE） scheme and overcomes PE＇s shortage. Its operation is demonstrated by using multi-seed, the group member is partitioned to many subgroups. While updating the SEK, computation is needed only in one of subgroups, the other of them will use the computation history to update their SEK. The key problems to design a MPE scheme application includes to find a feasible one way function as well as to generate a Strict Prime Number （SPN）. Those technologies with multi-seed and computation history concepts make MPE as a good choice in practical applications. A prototype test system is designed and solutions of all above mentioned problems are included in this proposed paper.     

Two aspects of evolutionary algorithms

In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints. Biography: Zbigniew Michalewicz(1952-), male, Professor, research interest: evolutionary computation