A parallel global-local mixed evolutionary algorithm for multimodal function optimization based on domain decomposition

This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly. Foundation item: Supported by the National Natural Science Foundation of China (60133010,60073043,70071042) Biography: Wu Zhi-jian(1963-), male, Associate professor, research direction: parallel computing, evolutionary computation.     

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 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.     

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.     

A gene-pool based genetic algorithm for TSP

Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is the construction of gene pool and how to apply it to GA. Different from standard GAs, Ge- GA aims to enhance the ability of exploration and exploitation by incorporating global search with local search. On one hand a local search called Ge- Lo-calSearch operator is proposed to improve the solution quality, on the other hand the modified Inver-Over operator called Ge- InverOver is considered as a global search mechanism to expand solution space of local minimal. Both of these operators are based on the gene pool. Our algorithm is applied to 11 well-known traveling salesman problems whose numbers of cities are from 70 to 1577 cities. The experiments results indicate that Ge- GA has great robustness for TSP. For each test instance, the average value of solution quality, found in accepted time, stays within 0. 001% from the optimum. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, and 60133010) Biography: Yang Hui ( 1979-), female, Master candidate, research direction; evolutionary computation.     

Buffer and Wiresizing Optimization under the Distributed RLC Model with Crosstalk Constraint

In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.     

A new algorithm for routing in ad-hoc network

In this paper, we propose a new algorithm for wireless mobile and ad-hoc network, which establishes dynamic cluster of nodes. The proposed algorithm, namely, the Mobility Sensitive Routing Protocol (MSRP), consists of routing in cluster and routing between clusters. Ad-hoc network can utilize MSRP to reduce information exchange and communication bandwidth, to shorten route acquisition delay, and to accommodate more nodes. Foundation item: Supported by the National Natural Science Foundation of China (60133010,60073043,70071042). Biography: Zhang Jian (1976-), male, Ph. D candidate. Lecturer, research direction: computer network, network optimization.     

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.     

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     

一种新的快速进化策略的设计与实现

对遗传程序设计思想进行拓展 ,通过对传统的进化策略进行改进 ,设计出一种新的快速全局寻优算法 ,该算法克服了传统的进化策略的缺点。实验表明这种新算法收敛速度快、有极强的避免局部极值的全局优化能力。     

HMM in predicting protein secondary structure

We introduced a new method—duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study, we divide the basic second structure of protein into three parts: H (α-Helix), E (β-sheet) and O (others, include coil and turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino acids (these interaction were represented by transmit probability), and we use genetic algorithm to determine the model parameters. After improving on the model and fixed on the parameters of the model, we write a program HMMPS. Our example shows that HMM is a nice method for protein secondary structure prediction. Foundation item: Supported by the National Natural Science Foundation of China (30170214) Biography: Huang Jing (1977-), female, Master candidate, research direction: bioinformatics.     

Finding global minima with a new dynamical evolutionary algorithm

A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel features are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results. Foundation item: Supported by the National Natural Science Foundation of China (No. 60133010, NO. 60073043 and No. 700/1042) Biography: Zou Xiu-fen(1996-), female, Ph. D candidate, Associate professor, research direction: evolutionary computing, parallel computing.     

Verifiable （ t, n） Threshold Signature Scheme Based on Elliptic Curve

Based on the difficulty of solving the ECDLP (elliptic curve discrete logarithm problem) on the finite field, we present a (t, n) threshold signature scheme and a verifiable key agreement scheme without trusted party. Applying a modified elliptic curve signature equation, we get a more efficient signature scheme than the existing ECDSA (ellipticcurve digital signature algorithm) from the computability and security view. Our scheme has a shorter key, faster computation, and better security.     

A new evolutionary algorithm for function optimization

A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory. Foundation item: Supported by the National Natural Science Foundation of China (No. 69635030), National 863 High Technology Project of China, the Key Scientific Technology Development Project of Hubei Province. Biography: GUO Tao(1971-), male, Ph D, research interests are in evolutionary computation and network computing.     

Analysis and improvement of TCP congestion control mechanism based on global optimization model

Network flow control is formulated as a global optimization problem of user profit. A general global optimization flow control model is established. This model combined with the stochastic model of TCP is used to study the global rate allocation characteristic of TCP. Analysis shows when active queue management is used in network TCP rates tend to be allocated to maximize the aggregate of a user utility functionU _s (called,U _s fairness). The TCP throughput formula is derived An improved TCP congestion control mechanism is proposed. Simulations show its throughput is TCP friendly when competing with existing TCP and its rate change is smoother. Therefore, it is suitable to carry multimedia applications. Foundation item: Supported by the 95 National Defense Project (31.1.2.3) Biography: Pan Li (1974-), male, Ph. D. candidate, research direction: broadband networks, congestion control and IP quality of service.     

Mining compressed frequent subtrees set

The number of frequent subtrees usually grows exponentially with the tree size because of combinatorial explosion. As a result, there are too many frequent subtrees for users to manage and use. To solve this problem, we generalize a compressed frame based on δ-cluster to the problem of compressing frequent-subtree sets, and propose an algorithm RPTlocal which can mine compressed frequent subtrees set directly. This algorithm sacrifices the theoretical bounds but still has good compression quality. By pruning the search space and generating frequent subtrees directly, this algorithm is also efficient. Experiment result shows the representative subtrees mining by RPTlocal is almost two orders of magnitude less than the whole collection of the closed subtrees, and is more efficient than CMtreeMiner, the algorithm for mining both closed and Maximal frequent subtrees. Foundation item: Supported by the National Natural Science Foundation of China (70371015)     

Genetic Algorithm-Based Approaches for Optimizing S-Boxes

0 Introduction Being as unique nonlinear components of block ci- pher algorithms, S-boxes provide the most important confusion effect, and directly influence the security of the algorithms. There are many ways to construct S-boxes[1-5]. On one hand, diffe…     

A binary images watermarking algorithm based on adaptable matrix

A watermarking algorithm of binary images using adaptable matrix is presented. An adaptable matrix is designed to evaluate the smoothness and the connectivity of binary images. The watermark is embedded according to the adaptable matrix in this algorithm. In the proposed watermarking algorithm, each image block implements a XOR operation with the binary adaptable matrix, which has the same size with the image block, and in order to embed the watermark data, a multiplication operation are also implemented with the weight matrix. The experimental results show that proposed scheme has a good performance.     

Gomory＇s Method Based on the Objective Equivalent Face Technique

This paper discusses a re-examinatlon of dual methods based on Gomory＇s cutting plane for the solution of the integer programming problem, in which the increment of objection function is allowed as a pivot variable to decide the search direction and stepsize. Meanwhile, we adopt the current equivalent face technique so that lattices are found in the discrete integral face and stronger valid inequalities are acquired easily.     

]Modeling dynamic systems by using the nonlinear difference equations based on genetic programming

When acquaintances of a model are little or the model is too complicate to build by using traditional time series methods, it is convenient for us to take advantage of genetic programming (GP) to build the model. Considering the complexity of nonlinear dynamic systems, this paper proposes modeling dynamic systems by using the nonlinear difference equation based on GP technique. First it gives the method, criteria and evaluation of modeling. Then it describes the modeling algorithm using GP. Finally two typical examples of time series are used to perform the numerical experiments. The result shows that this algorithm can successfully establish the difference equation model of dynamic systems and its predictive result is also satisfactory. Foundation item: Supported by Foundation for University Key Teacher by the Ministry of Education of China Biography: Liu Min ( 1978-), female, Master candidate, research derection: application mathematics.