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     

Asynchronous parallel evolutionary algorithms for constrained optimizations

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. National Laboratory for Parallel and Distributed Processing Foundation item: Supported by the Natonal Natural Science Foundation of China (No. 70071042, 50073043), the National 863 Hi-Tech Project of China (No. 863-306-ZT06-06-3) and the National Laboratory for Parallel and Distributed Processing. Biography: Kang Li-shan (1934-), male, Professor, research interests: parallel computing and evolutionary computation.     

Numerical integration method in analysis of wire antennas

The numerical evaluation of an integral is a frequently encountered problem in antenna analysis. A special Gauss-Christoffel quadrature formula for nonclassical weight function is constructed for solving the pseudo-singular integration problem arising from the analysis of thin-wire antennas. High integration accuracy is obtained at comparable low computation cost by the quadrature formula constructed. This integration method can be also used in other electromagnetic integral equation problems. Supported by the 863 High Technology Project of China (No. 863-818-01-02) and the National Science Foundation of Hubei Province (No. 97J002) Yang Shaolin: born in 1975, M. S graduate student.     

A genetic algorithm for the flowshop scheduling problem

The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within the same time. Supported by the National Natural Science Foundation of China and 863 High Technology Project of China Qi Yuesheng: born in 1967, Ph. D.     

Balance between exploration and exploitation in genetic search

Genetic search plays an important role in Evolutionary Computation (EC). There are two important issues in the evolution process of the genetic search: exploration and exploitation. Exploration is the creation of population diversity by exploring the search space; exploitation is the reduction of the diversity by focusing on the individuals of higher fitness, or exploiting the fitness information (or knowledge) represented within the population. We theoretically analyze the impact of the genetic operators on the balance. In order to further explain the impact, some results of our research on ESs are showed. Finally we conclude that to make the algorithm more efficient, it is important to strike a balance between these two factors. Supported in part by the National Natural Science Foundation of China (No. 69635030) and Doctoral Programme Foundation of China, and National 863 High Technology Project of China Lin Hansheng: born in 1975, Master     

Secure localization algorithm based on node cooperative for sensor networks

As to the safety threats faced by sensor networks （SN）, nodes limitations of computation, memory and communication, a secure location algorithm （node cooperative secure localization, NCSL） is presented in this paper. The algorithm takes the improvements of SN location information security as its design targets, utilizing nodes＇ cooperation to build virtual antennae array to communicate and localize, and gains arraying antenna advantage for SN without extra hardware cost, such as reducing multi-path effects, increasing receivers＇ signal to noise ratio and system capa- bility, reducing transmitting power, and so on. Simulations show that the algorithm based on virtual antennae array has good localization ability with a at high accuracy in direction-of-arrival （DOA） estimation, and makes SN capable to resist common malicious attacks, especially wormhole attack, by using the judgment rules for malicious attacks.     

The harmonic decomposition reconstruction for the exponential radon transform

The exponential Radon transform, a generalization of the Radon transform, is defined and studied as a mapping of function spaces. It is represented in terms of Fourier transform of its domain and range, and this leads to the harmonic decomposition reconstruction. The results are similar results of Tretiak and Metz. Foundation item: Supported by the National Natural Science Foundation of China (No. 19971064), Key Project of Science and Technology of Hubei Province Education Committee. Biography: Wang Jin-ping (1963-), male, Ph.D. candidate, research direction: numberical solution of singular integral equation and integral transformation etc.     

Using evolutionary computation to solve problems in nonparametric regression

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. Supported by the National Natural Science Foundation and 863 High Technology Project of China Ding Lixin: born in 1967, Ph.D. graduate student     

Using evolutionary computation to solve problems in nonparametric regression

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. Supported by the National Natural Science Foundation and 863 High Technology Project of China Ding Lixin: born in 1967, Ph.D. graduate student     

A two-level subspace evolutionary algorithm for solving multi-modal function optimization problems

In this paper, a new algorithm for solving multimodal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, 60133010). Biography: Li Yan( 1974-), female, Ph. D candidate, research interest: evolutionary computation.     

Double-Talk Detection Algorithm Based on the Ι2 Norm

Echo canceller generally needs a double-talk detector which is used to keep the adaptive filter from diverging in the appearance of near-end speech. In this paper we adopt a new double-talk detection algorithm based on ι2 norm to detect the existence of near-end speech in an acoustic echo canceller. We analyze this algorithm from the point of view of functional analysis and point out that the proposed double-talk detection algorithm has the same performance as the classic one in a finite Banach space. The remarkable feature of this algorithm is its higher accuracy and better computation complexity. The fine properties of this algorithm are confirmed by computer simulation and the application in a multimedia communication system.     

A new evolutionary algorithm for solving multi-objective optimization problems

Multi-objective optimization is a new focus of evolutionary computation research. This paper puts forward a new algorithm, which can not only converge quickly, but also keep diversity among population efficiently, in order to find the Pareto-optimal set. This new algorithm replaces the worst individual with a newly-created one by “multi-parent crossover”. so that the population could converge near the true Pareto-optimal solutions in the end. At the same time, this new algorithm adopts niching and fitness-sharing techniques to keep the population in a good distribution. Numerical experiments show that the algorithm is rather effective in solving some Benchmarks. No matter whether the Pareto front of problems is convex or non-convex, continuous or discontinuous, and the problems are with constraints or not, the program turns out to do well. Foundation item: Supported by the National Natural Science Foundation of China(60133010, 60073043, 70071042) Biography: Chen Wen-ping ( 1977-), female, Master candidate, research direction: evolutionary computation.     

Differentiation of human embryonic stem cells along a hepatocyte lineage and its application in liver regeneration

Hepatocyte transplantation and bioarUficial liver （BAL） as alternatives to liver transplantation offer the possibility of effective treatment for many inherited and acquired hepatic disorders. Unfortunately, the limited availability of donated livers and the variability of their derived hepatocytes make it difficult to obtain enough viable human hepatocytes for the hepatocyte-based therapies. Embryonic stem cells （ESCs）, which could be isolated directly from the blastocyst inner cell mass, have permanent self-renewal capability and developmental pluripotency and therefore might be an ideal cell source in the treatment of hepatic discords. However, differentiation of hESCs into hepatocytes with significant numbers remains a challenge. This review updates our current understanding of differentiation of ESCs into hepatic lineage cells, their future therapeutic uses and problems in liver regeneration.     

Design and Implementation of FAQ Automatic Return System Based on Similarity Computation

FAQ （frequently asked question） is widely used on the Internet, but most FAQ＇s asking and answering are not automatic. This paper introduces the design and imple mentation of a FAQ automatic return system based on semantic similarity computation, including computation model choo sing, FAQ characters analyzing, FAQ data formal expressing, feature vector indexing, and weight computing and so on. According to FAQ features of sentence length short, two mapping, strong domain characteristics etc. Vector Space Model with special semantic process was selected in system, and corresponding algorithm of similarity computation was proposed too. Experiment shows that the system has a good performance for high frequent and common questions.     

Parallel evolutionary modeling for nonlinear ordinary differential equations

We introduce a new parallel evolutionary algorithm in modeling dynamic systems by nonlinear higher-order ordinary differential equations (NHODEs). The NHODEs models are much more universal than the traditional linear models. In order to accelerate the modeling process, we propose and realize a parallel evolutionary algorithm using distributed CORBA object on the heterogeneous networking. Some numerical experiments show that the new algorithm is feasible and efficient. Foundation item: Supported by the National Natural Science Foundation of China (No. 70071042 and No. 60073043) Biography: Kang Zhuo (1970-), male, Lecturer, research interest: network computing and evolutionary computation.     

A heuristic algorithm to solve the task partition problem in MDOCEM

This paper examines task partition problem in a Multiagent-based Distributed Open Computing Environment Model (MDOCEM). We first present a formal method to describe the task partition problem, then give a heuristic algorithm to solve the task partition problem that gives an approximate optimum solution. Foundation item: Supported partially by the National 863 High Technology Project (863-306-05-01-1B) Biography: He Yan-xiang (1952-), male, Professor, Research interests include distributed OS and distributed information processing, distributed software engineering, parallel compiler.     

Solving integer programming by evolutionary soft agent

Many practical problems in commerce and industry involve finding the best way to allocate scarce resources a-mong competing activities. This paper focuses on the problem of integer programming, and describes an evolutionary soft a-gent model to solve it. In proposed model, agent is composed of three components: goal, environment and behavior. Experimental shows the model has the characters of parallel computing and goal driving. Foundation item: Supported by the National Natural Science Foundation of China( 60205007) , Natural Science Foundation of Guangdong Province(001264), Research Foundation of Software Technology Key Laboratory in Guangdong Province and Research Foundation of State Key Laboratory for Novel Software Technology at Nanjing University Biography: Yin Jian ( 1968-), male, Associate professor, research direction: artificial intelligence, data mining.     

Genetic algorithm for Chinese postman problems

Chinese Postman Problem is an unsettled graphic problem. It was approached seldom by evolutionary computation. Now we use genetic algorithm to solve Chinese Postman Problem in undirected graph and get good results. It could be extended to solve Chinese postman problem in directed graph. We make these efforts for exploring in optimizing the mixed Chinese postman problem. Foundation item: Supported by the National Natural Science Foundation of China (60133010, 70071042) Biography: Jiang Hua(1974-), male, Master candidate, research direction: Evolutionary computation.     

Testing Forms in Web Applications Automatically

Forms enhance both the dynamic and interactive abilities of Web applications and the system complexity. And it is especially important to test forms completely and thoroughly. Therefore, this paper discusses how to carry out the form testing by different methods in the related testing phases. Namely, at first, automatically abstracting forms in the Web pages by parsing the HTML documents; then, ohtai ning the testing data with a certain strategies, such as by requirement specifications, by mining users＇ hefore input informarion or by recording meehanism; and next executing the testing actions automatically due to the well formed test cases; finally, a case study is given to illustrate the convenient and effective of these methods.     

Stable initialization scheme for K -means clustering

Though K-means is very popular for general clustering, its performance which generally converges to numerous local minima depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor (RNN) search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of proposed algorithm to K-means clustering algorithm is demonstrated. Experiment is carried out on several popular datasets and the results show the advantages of the proposed method. Foundation item: Supported by the National Natural Science Foundation of China (60503020, 60503033, 60703086), the Natural Science Foundation of Jiangsu Province (BK2006094), the Opening Foundation of Jiangsu Key Laboratory of Computer Information Processing Technology in Soochow University (KJS0714) and the Research Foundation of Nanjing University of Posts and Telecommunications (NY207052, NY207082)