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 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.     

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.     

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.     

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     

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.     

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.     

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 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.     

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.     

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 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.     

A new evolutionary algorithm based on the decimal coding

Traditional Evolutionary Algorithm (EAs) is based on the binary code, real number code, structure code and so on. But these coding strategies have their own advantages and disadvantages for the optimization of functions. In this paper a new Decimal Coding Strategy (DCS), which is convenient for space division and alterable precision, was proposed, and the theory analysis of its implicit parallelism and convergence was also discussed. We also redesign several genetic operators for the decimal code. In order to utilize the historial information of the existing individuals in the process of evolution and avoid repeated exploring, the strategies of space shrinking and precision alterable, are adopted. Finally, the evolutionary algorithm based on decimal coding (DCEAs) was applied to the optimization of functions, the optimization of parameter, mixed-integer nonlinear programming. Comparison with traditional GAs was made and the experimental results show that the performances of DCEAS are better than the tradition GAs. Foundation item: Supported by the National Natural Science Foundation of China (No. 69703011) Biography: Dong Wen-yong (1973-), male Ph. D. candidate, research direction: parallel algorithms, evolutionary computation, computer simulation.     

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.     

Parameter optimization of electric bus transmission system based on dynamical evolutionary algorithm

The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle (EV). As a class of heuristic algorithms, Dynamical Evolutionary Algorithm (DEA) is suitable to solve multi-objective optimization problems. This paper presents a new method to optimize the transmission ratio using DEA. The fuzzy constraints and objective function of transmission ratio are established for parameter optimization problem of electric bus transmission. DEA is used to solve the optimization problem. The transmission system is also designed based on the optimization result. Optimization and test results show that the dynamical evolutionary algorithm is an effective method to solve transmission parameter optimization problems.     

Some massively parallel algorithms from nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes. Foundation item: Supported by the National Natural Science Foundation of China (No. 60133010, No. 70071042, No. 60073043) and National Laboratory for Parallel and Distributed Processing Biography: Li Yan (1974-), female, Ph. D candidate, research direction: evolutionary computation.     

基于QPSO和MATLAB优化资金投资组合

量子粒子群优化算法(QPSO)是一种基于粒子群优化算法(PSO)的进化算法,它收敛速度快、规则简单、易于编程实现;Matlab是国际控制界公认的标准计算软件。采用QPSO对资金组合投资的多目标问题进行优化,使用Matlab编程,解决了传统方法难以解决的问题,仿真实验表明采用本方法能对资金投资组合问题提出较好的优化决策。     

Dynamically Computing Approximate Frequency Counts in Sliding Window over Data Stream

This paper presents two one-pass algorithms for dynamically computing frequency counts in sliding window over a data stream-computing frequency counts exceeding user-specified threshold ε. The first algorithm constructs subwindows and deletes expired sub-windows periodically in sliding window, and each sub-window maintains a summary data structure. The first algorithm outputs at most 1/ε ＋ 1 elements for frequency queries over the most recent N elements. The second algorithm adapts multiple levels method to deal with data stream. Once the sketch of the most recent N elements has been constructed, the second algorithm can provides the answers to the frequency queries over the most recent n （ n≤N） elements. The second algorithm outputs at most 1/ε ＋ 2 elements. The analytical and experimental results show that our algorithms are accurate and effective.     

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:     

An improved moving mesh algorithm

We consider an iterative algorithm of mesh optimization for finite element solution, and give an improved moving mesh strategy that reduces rapidly the complexity and cost of solving variational problems. A numerical result is presented for a 2-dimensional problem by the improved algorithm. Foundation item: Supported by the National Natural Science Foundation of China(No. 19771062) Biography: Cheng Jian(1977-), male, Master candidate, research interests: the numerical solution of PDE.