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     

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.     

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.     

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.     

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.     

A Note on the Multifractal Decomposition of Directed Graph Self Similar Sets

We investigated the directed graph self similar sets under some weak overlapping condition. We get the multifractal decomposition formulas for these sets, i. e.,dim Ku^a DimKu^a =f(a), where fis the multifractal spectral function of the directed graph self similar measure. Especially, the results improve that of Edgar and Mauldin to the case which allows certain overlapping.     

Deviation inequality for the number of k -cycles in a random graph

We study the number of k-cycles in a random graph G(n, p). We estimate the probability that a random graph contains more k-cycles than expected. In this case, the usual martingale inequality with bounded difference is not effective. By constructing a variable that approximates to the number of k-cycles in a random graph and using a new and extensive martingale inequality, we get the results in this paper. Foundation item: Supported by the National Natural Science Foundation of China (10571139)     

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 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.     

An Improved Genetic Algorithm for Problem of Genome Rearrangement

In view of the fact that the problem of sorting unsigned permutation by reversal is NP-hard, while the problem of sorting signed permutation by reversal can be solved easily, in this paper, we first transform an unsigned permutation of length n,π （π1 ,… ,πn）, into a set S（π） containing 2^n signed permutations, so that the reversal distance of π is equal to the reversal distance of the optimal signed permutation in S（π）. Then analyze the structural features of S（π） by creating a directed graph and induce a new computing model of this question. Finally, an improved genetic algorithm for solving the new model is proposed. Experimental results show that the proposed model and algorithm is very efficient in practice.     

Determination of ammonium ion in lake water by voltammetry

0　IntroductionDetectionofammoniumionisveryimportantinmanyfields,suchashealthcare,agriculture ,cultivationandenviron mentalpollutioncontrol.Severalanalyticalmethodshavebeende velopedforammoniumdeterminationincludingionchromatography ,spectrophotometry,fluorimetryandchemiluminescence .MostofthemarebasedonspectrophotometryusingtheBerthelot reactionbetweenammonium ,phenol,andhypochloriteleadingtothefor mationofanindophenoldye[1 ] .Thereactiontimeisaboutanhour,andsomedissolvedorganicnitrogencompo…     

A deterministic construction of irregular LDPC codes

In order to solve high encoding complexities of irregular low-density parity-check (LDPC) codes, a deterministic construction of irregular LDPC codes with low encoding complexities was proposed. And the encoding algorithms were designed, whose complexities are linear equations of code length. The construction and encoding algorithms were derived from the effectively encoding characteristics of repeat-accumulate (RA) codes and masking technique. Firstly, the new construction modified parity-check matrices of RA codes to eliminate error floors of RA codes. Secondly, the new constructed parity-check matrices were based on Vandermonde matrices, this deterministic algebraic structure was easy for hardware implementation. Theoretic analysis and experimental results show that, at a bit-error rate of 10×10⁻⁴, the new codes with lower encoding complexities outperform Mackay’s random LDPC codes by 0.4–0.6 dB over an additive white Gauss noise (AWGN) channel. Foundation item: Supported by the National Natural Science Foundation of China(60496315, 60572050)     

Two-phase genetic algorithm applied in the optimization of multi-modal function

This paper presents a two-phase genetic algorithm (TPGA) based on the multi-parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population’s evolvement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions) usually we can obtain all the global optimal solutions. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043,60133010) Biography: Huang Yu-zhen ( 1977-), female, Master candidate, research direction; evolution computation.     

Extracting multiple layers from data having graph structures

Much data such as geometric image data and drawings have graph structures.Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables, and a substitution over term graphs which is graph we also define a multiple layer for S as a pair (D,O) of a set D of term graphs and a list of substitutions. Secondly, for a graph G and a set S of graphs, we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S, respectively. Finally, we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.     

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)     

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     

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.     

Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section

This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.     

Incremental Web Usage Mining Based on Active Ant Colony Clustering

To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.     

Modeling and Performance Analysis of Dispatching Policies in Dispatcher-Based Web Server Cluster

There are two kinds of dispatching policies in content-aware web server cluster; segregation dispatching policy and mixture dispatching policy. Traditional scheduling algorithms all adopt mixture dispatching policy. They do not consider that dynamic requests＇ serving has the tendency to slow down static requests＇ serving, and that different requests have different resource demands, so they can not use duster＇s resource reasonably and effectively. This paper uses stochastic reward net （SRN） to model and analyze the two dispatching policies, and uses stochastic Petri net package （SPNP） to simulate the models. The simulation results and practical tests both show that segregation dispatching policy is better than mixture dispatching policy. The principle of segregation dispatching policy can guide us to design efficient scheduling algorithm.